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![IBM’s stack builds confidence among IBM specifically assigns technical own methodology and highlights
managers of existing warehouse account managers to support that the traditional enterprise data
implementations that the product is accounts). Additionally, IBM’s focus warehouse [EDW] is vital to all data
evolving as new demands for these on prospect qualification resulted in warehouse strategies including as a
two components of the logical data a higher growth in 2011 vs. 2009 to base component for the LDW.
warehouse emerge. 2010 for all of its products. This was IBM’s first incarnation of
Additionally, for Smart • The overall effect is that referenced the LDW approach. The market
Consolidation – rather than customers are confident regarding is acknowledging that the EDW
developing tooling in isolation, IBM release dates and the road map. does not have to be the center of
focused on tooling that existed in Customers list concurrency, the strategy but will be significant.
its Information Integration portfolio scalability, performance optimization However, the justification for
(InfoSphere BluePrint Director). and support as positives and were the LDW and evolving existing
This resulted in improvements in the most often repeated phrases warehouses or replacing them
the area of integration, including but in the reference survey in 2011. will be difficult at first because
not limited to the common Data References elaborated by indicating it appears to supporters of
Warehouse Packs and Models now that partitioning, compression and traditional data warehouses to
supported on DB2 and Netezza reduced administrative hours all be a radical departure from their
platforms alike. contribute to their experience to beloved traditional data warehouse
• IBM combines product sales with support optimized performance. practices. Gartner’s own research
solution services. This market At the same time, some references indicates that the LDW approach is
demands a widely varied level reported that optimization of quickly emerging as the newest data
of sophistication and knowledge queries should be targeted rather warehouse best practice. Gartner
depending on each client than being forced to optimize every anticipates the LDW will become
organization’s maturity in analytics single query because the system is a best practices approach during
and information management. As able to engage a solid query plan for 2013-2015. With market leadership
noted in the overview, the data execution. This evaluation considers there is risk commensurate with the
warehouse market in 2011 has the LDW concept to be innovative, anticipated rewards. IBM will need
multiple visions for the future. but has yet to see a wider embrace to continue their careful education
IBM has embraced the logical in the market. IBM’s early adoption message regarding their leadership
data warehouse (via “Smart of the LDW concept in both its approach in LDW practices. When
Consolidation”) approach while messaging and its product road engaging in an LDW approach
continuing to advance its technology map has established this vendor as with IBM, clients should insure
solutions and implementation an early resource for the market. they completely understand IBM’s
practices supporting traditional data However, the majority of the positioning for implementing this
warehousing architectures. market for data warehousing will solution.
Professional services available remain significantly focused on • Gartner inquiries report indicate
from IBM range from expert traditional solutions for a minimum that IBM data warehouse solutions
education through turnkey of the next three years. are also marketed and delivered in
solutions to managed services for isolation from each other. There are
data warehousing. Importantly, Cautions strategic reasons to continue such
where IBM leverages its services an approach with any acquisition,
• IBM has embraced the logical data
organization most, is in feeding but Netezza products tend to have
warehouse vision as the likely
field experiences into the overall their own niche in customers’ minds
successor to current best practices
data warehouse vision. In 2010, that is viewed as being separate and
in traditional data warehousing. The
clients reported that IBM’s support distinct from IBM (but Netezza’s
market has not yet determined if
appears disconnected from its growth was more than 30% in 2011,
it is ready to adopt this approach
product strategy – this improved in which is faster than its previous
as the new vision for the data
2011 with an even larger reference growth rate as an independent
warehouse and abandon 20 years
base reporting. This does not mean company).
of traditional best practices.
the issue has been resolved, but it IBM’s professional services have As a result, IBM customers often
appears that IBM’s focus on solution experience in delivering various engage only part of the organization
services is paying off (for example, aspects of the LDW under its for solutions and at least in the
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![customer’s minds, eliminate the compressed DBMS. The company Infobright also released an option
others. This creates both marketing provides both an open-source version for the Enterprise Edition called the
and sales process challenges. This (Infobright Community Edition [ICE]) Distributed Load Processor (DLP)
is not an issue with shortlisted and a commercial version (Infobright which allows for the parallel loading
solutions (IBM should recommend Enterprise Edition [IEE]). Infobright has of data into the system at very high
one solution or another), but does approximately 200 customers worldwide. speeds. Infobright has also added
carry over into the solution delivery connectivity to Hadoop MapReduce
team and IBM is missing some Strengths for the processing of “Big data.”
opportunities for the different parts • Infobright remains one of the only This is extremely important to
of the sales organization to leverage column-store DBMS in the open- the machine-generated data world
each other. IBM has implemented source software environment. as much of this data is stored in
organizational changes intended to Its revenue is generated from Hadoop or other such file systems
address these issues. the Enterprise Edition (using a and needs to be extracted into a
Netezza and IBM personnel do commercial license, rather than a DBMS for processing.
interact and coordinate with General Public License [GPL]) with • Our customer references are clear
each other behind the scenes. a subscription support model based on several points. Infobright is
A marketing solution would on the amount of SSED stored in extremely fast compared to other
simply begin branding software the system. As we stated in 2011, systems, including MySQL. Reports
and hardware combinations for Infobright decided in mid-2010 to of up to an average 500% increase
limited purposes. However, IBM focus on operational technology in performance over MySQL
will choose the more difficult (and data (which it calls machine- deployments have been reported.
more appropriate) solution of generated data). This encompasses We believe this is not only from
creating an educational sales and data from sources such as smart the column-store design, but also
implementation process which meter data (in the utilities space), the Knowledge Grid. References
will demonstrate how software customer data records (in the telco suggest that Infobright is replacing
and hardware capabilities can be space) and clickstream data from an existing MySQL environment
leveraged effectively to support Internet interactions. with great gains in stability,
each use case. This focus has helped Infobright compression and performance.
• IBM customers report (via inquiry during 2011 where its customer Some cases report a year or more
and reference survey results) base has grown to more than 200 without an outage.
a scattering of intermittent direct and OEM channel customers. Finally, many references state that
and irregular issues with Not only has this focus increased simplicity is a factor in their choice
product performance or their customers, but has also attracted to use Infobright. We also believe
implementation experience. Some a number of additional OEMs this will interest OEMs that want to
of these are possibly attributed to (now accounting for approximately build-in Infobright to their existing
the implementation process and 40% of customers). This, along systems for resale. The simplicity
not the products. However, these with partnerships with Pentaho, of management, scalability and
same customers report that IBM Jaspersoft, Talend and others, will compression all interest the OEM
support addresses these issues with help the company grow substantially looking for a DBMS to embed that
efficiency. Nonetheless, as with faster than direct sales only. requires little support on their part.
any IT products, an assumption • Infobright has several unique The focus on machine-generated
that appliances or certified technologies in the DBMS. In data has been important to
configurations alleviate all issues is addition to the column-store file Infobright, but we believe that the
incorrect. Most issues are irregular system for MySQL, the Knowledge future will greatly depend on the
in nature and IBM support is Grid in-memory metadata store company’s ability to leverage these
intimately involved in the resolution is a major differentiator for OEM partners.
process. Infobright, as this product analyzes
queries to minimize the number Cautions
Infobright of “data packs” that have to be • One of the biggest challenges for
Infobright (www.infobright.com) has decompressed to give a result (data a small vendor is to focus on what
offices in Canada, Europe and the packs are the compressed domains/ they do well. Infobright has done
U.S. and offers a combination of a regions of data in Infobright’s this with machine-generated data.
column-vectored DBMS and a fully offering).
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![all Real Application Clusters [RAC] Oracle references also report continuing improvement. Finally, the
nodes in the cluster are treated as that the products and appliances Oracle licensing model continues
a single, large memory pool and appear to advance with additional to be an issue for these reference
data is transformed/optimized for functionality at the desired pace customers who complain of
different storage in memory, rather for the expanding requirements difficulty in cost planning.
than on disk). Exadata has Exadata (functionality, data volume scaling • Oracle faces several challenges
Smart Scan (to offload some and the types and number of with its LDW vision, but it has
DBMS functionality to the storage anticipated queries). In 2011, time to respond and it is possible
server), Exadata Hybrid Columnar references showed that some of the that an appliance can also contain
Compression (which reduces previously reported issues regarding a complete array of services
storage requirements and increases management tools were resolved. management, application servers
performance) and Exadata Smart The result is that Oracle customers and data management platform to
Flash Cache (up to about 5 TB have a pre-disposition to using manage a logical data warehouse;
of flash memory to optimize data more Oracle products and this or that many appliances can be
access and queries). includes data warehousing, which configured to address the LDW.
According to Gartner’s clients and increases market sales generally and Oracle’s current strategy for
client references (interviews began even more specifically, creates a analytics information management
in late 2010, when Gartner spoke solid pipeline with existing Oracle remains tightly bound to a
with over 100 Oracle Exadata customers. repository-based implementation
warehouse implementations in the approach. Oracle Big Data
past 18 months), Oracle Exadata Cautions Appliance includes its own NoSQL
exhibits up to a tenfold increase in • Oracle clients are practical and database (based on BerkeleyDB)
average performance, compared realistic in that they recognize the and an Apache/Hadoop distribution.
with a similar workload running consistent strengths of products Prior to the publication of this
Oracle on stand-alone hardware. and that any issues are inconvenient Magic Quadrant analysis, Oracle
It should be noted this is increased or nuisances. Oracle has the announced that Oracle Big Data
performance measuring Oracle most customer references of any Appliance incorporates the
against Oracle. Oracle Exadata data warehouse platform within Cloudera version of Hadoop.
is not required to deploy a data Gartner’s client base and so has Oracle already has the capability
warehouse on Oracle Database, the greatest mix of comments in its various tools (such as Oracle
but many of the solution design across these references. However, Fusion Middleware, Oracle Data
issues solved by Oracle Exadata three types of issues are commonly Integrator and various DBMS
must then be addressed by clients’ reported directly by customers. solutions) to address distributed
implementation teams on their First, frequent “bugs” or software data and distributed processing,
own. issues are reported in new releases, but its primary message for
• Oracle’s customer references followed by a difficult patching warehousing still emphasizes the
indicate the product is highly process. Second, when customers repository management approach in
stable, consistently meets the experience specific performance favor of virtualization or distributed
performance requirements of their issues involving complex queries, processes. This combination of
use case needs, is easily supported they are difficult to resolve due challenges and events will not
by a widely available skill base to “poor visibility into the actual necessarily force Oracle to isolate
in the market and has a solid bottleneck.” on a single vision for the market,
list of features (such as Exadata Some improvements in customer but the temptation to do so will be
Hybrid Column Compression, management experiences are significant. During 2012 to 2013,
Oracle Partitioning, Oracle RAC reported, but according to it will be important for Oracle to
and Oracle Automated Storage references, these issues result establish its future vision for this
Management). The net result is in somewhat higher efforts in market.
that implementers who choose to administration and management of • As a new hardware vendor,
proceed with Oracle will be able the environment. Gartner considers Oracle may pursue the vision of
to proceed in a straightforward Oracle’s support regarding these hardware exclusivity or it may
manner. issues to be effective and 2011 saw continue in its agnostic view of
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![Once vendors have established their • The rising demand for combining • A DBMS has to support workloads
positions, the major tussle will begin, structured and content information. ranging from simple to complex
toward the end of 2013. It is becoming and to manage mixed workloads in
clearer that this will represent a major Organizations have expressed an interest many different combinations.
upheaval in the market, one that the in technical solutions that is starting to • Users are getting better at creating
larger vendors need to prepare for and erode the 2009 effect where everyone specific SLAs and the implications of
that will give smaller vendors a market sought vendor financial viability. A spirit not meeting them are more serious.
opportunity. of experimentation, pilot schemes and
prototypes has re-emerged. Organizations The data warehouse DBMS has evolved
The new analytics infrastructure is a are reminded to closely align their from being an information store to a
combination of services, platforms, analytics strategies and vendor road maps support for reporting and traditional
repositories, metadata and optimization when choosing vendors. BI platforms and now into a broader
techniques which all work in concert.
analytics infrastructure supporting
The “data warehouse” will become “data The data warehouse DBMS market is operational analytics, performance
warehousing” – again. The concept of a complex, with a mix of mature and new management and other new applications
single grand repository managing all the products. Its complexity reflects many and uses, such as operational BI, real-
information for analytics use cases will be factors, such as: time fraud detection or consumer
increasingly challenged and near 2017, a
• The need for DBMS systems to experience personalization and
new infrastructure of highly-distributed
support database sizes ranging from operational technologies (technologies
processes and information assets will have
the 2 terabytes to 1+ petabytes. that stream data from devices such as
emerged.
• The complexity of data in data smart meters). Organizations are adding
warehouses, not only in terms additional workloads with OLTP access
As described in “The State of Data
of interrelationships, but also of and data loading latency is falling to near-
Warehousing in 2011” several aspects of
desired data types. continuous loading.
this battle are emerging:
• The combination of repositories, • The fact that data warehouses are
There are many other aspects to the
data virtualization and data buses built on many different hardware
data warehouse DBMS market, such
is now possible, given the state of and operating systems, which a
as pricing models, geographic reach,
hardware technology. DBMS needs to support.
partner channels, third-party software
• The reduced influence of BI • The growing and regularly changing partnerships and data warehouse services
platform optimization, in favor of variety of operations performed in (see “The State of Data Warehousing,
DBMS optimization. data warehouses, which requires 2012” [forthcoming] and “Data
continuous management of the Warehousing Trends for the CIO, 2011-
• The increasing influence of master
DBMS. 2012” for further information on these
data management and data quality.
trends).
• The demand for cloud solutions.
Source: Gartner Research, G00219281,
Mark A. Beyer, Donald Feinberg, Merv Adrian,
Roxane Edjlali, 6 February 2012
Note 1
Definition of a Data Warehouse Appliance
A prepackaged or preconfigured, balanced set of hardware (servers, memory, storage and input/output
channels), software (OS, DBMS and management software), service and support, sold as a unit with
built-in redundancy for high availability and positioned as a platform for data warehousing. Further, it
must be sold on the basis of the amount of SSED (“raw data”) to be stored in the data warehouse and
not of configuration (for example, the number of servers or storage spindles).
Our performance criteria have some flexibility to accommodate vendors that have several variations,
based on desired performance SLAs and the type of workload intended for the appliance. Our primary
concern is that the user (buyer) cannot change the configuration due to budgetary issues, thereby
adversely affecting the performance of the appliance.
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Sybase IQ Big Data
SAP Sybase IQ is an analytic database management system designed for advanced analytics, data warehousing, and business intelligence environments. It can handle massive volumes of structured and unstructured data. Key features include its column-based storage which provides faster performance than row-based databases, massively parallel processing capabilities, and the ability to integrate results from Hadoop frameworks and execute R libraries and predictive models within the database. These features allow Sybase IQ to meet the growing needs of analyzing large and diverse datasets, commonly referred to as big data.
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Sybase IQ Big Data
- 1. Sybase IQ Issue 1 Introduction 2012 “Big Data” is the new hot topic for IT managers, and is causing quite a panic amongst some organizations; but, there is no need to panic, Big Data can be looked upon as Big Opportunity. IN THIS ISSUE With the data explosion companies now have access to more information than ever before – if the data can be exploited properly it can lead to a big competitive advantage. Introduction.........................1 With companies acquiring massive amounts of data in different forms from different sources, SAP Sybase IQ - Turning ranging from traditional channels with structured formats to social media channels with Big Data into a Big unstructured formats, it has changed the focus of analytics in the “real-world”. Throughout organizations there are changes in the way data is being analyzed – in marketing, the focus has Advantage............................. 2 shifted to digital channels – click streams and social media – to understand buying patterns, and Gartner Research: target marketing activities for maximum impact. In sales, the focus is on what we call “deal Magic Quadrant for Data DNA”, to correlate emails, meeting notes and chatter to assess the probability that a sales Warehouse Database deal will close. On the financial side, simulation is being used to predict margins and portfolio values; while on the operational side, machine data via sensors, and other kinds of digital data are Management Systems......... 5 being analyzed to track down operational inefficiencies – it’s no wonder companies are having About Sybase..................... 29 information overload and are at a loss as to how to manage the information let alone how to use that information intelligently. The key to Big Data is the ability to access and connect all the data no matter what type or where it came from, in order to achieve this you have to break the information silos that trap data – turning massive amounts of data into actionable insight while providing complete access to decision makers – creating an environment that offers “intelligence for everyone”. Featuring research from
- 2. SAP Sybase IQ– Advanced volume, variety and velocity of today’s Massive Scalability Analytics Platform for massive data needs and demands in a cost effective and attainable manner. With a state of the art query processor Big Data Sybase IQ thrives on heavy ad hoc query Sybase IQ is based on a three layer usages by large numbers of concurrent SAP Sybase IQ is an analytic DBMS architecture. A strong data management users – it’s designed to handle it. Built designed specifically for advanced layer is the foundation with a highly on PlexQ™ technology framework that analytics, data warehousing, and business compressed column store, and shared delivers a shared-everything massively intelligence environments. Able to work everything distributed MPP elastic cluster parallel processing (MPP) architecture with massive volumes of structured and that supports a variety of workloads and based on a columnar data store, it unstructured data it is ideally suited to Big active user community. The application delivers new levels of performance. Data. services layer sits above that to provide Unlike shared nothing solutions, a PlexQ a variety of drivers, APIs, web services, grid dynamically manages analytics Sybase IQ is built on an open, flexible and federation capabilities to empower workloads across an easily expandable column-store technology, unlike developers. And wrapped around these grid of computing resources dedicated to traditional relational databases, that store two technology layers, is a rich ecosystem different groups and processes, making data by row, slowly working through of BI tools, partner libraries, packaged it simpler and more cost-effective to each row of entire tables, clogging I/O applications, and data integration tools support growing volumes of data and channels, memory, and disk, Sybase IQ to give you an end to end solutions. (See rapidly growing user communities. uses a strategy called “vertical portioning” Figure 1) that stores data by column, reading only the columns of data used by the query. With PlexQ grid technology, enterprise Using columns, not rows, delivers a 10 to Centralized Access to All IT departments can more easily overcome 100 times performance boost compared Your Enterprise Data the scalability limitations of traditional to the traditional row-based approaches data warehouses. Organizations are – and Sybase IQ supports most of the Sybase IQ centralizes “Big Data” analysis now able to support user communities popular hardware and OS configurations. of massive volumes of structured and across the enterprise, and integrate unstructured data together using a analytics into business workflows. And, Big Data is not new to Sybase. Sybase IQ wide range of advanced techniques it’s easy to leverage advanced analytics has been building on the vision of a big and technologies – offering a data type within applications by using hundreds of data analytics platform for several years agnostic engine Sybase IQ doesn’t care algorithms and data mining models that now – the new Sybase IQ 15 family has what format of data you have or even can run inside Sybase IQ. been a steady progression of releases where it came from. Whether it be Elastic computing of logical servers in the that have followed a conscious roadmap, structured in a defined format, semi- PlexQ™ technology framework within each one adding innovations that build structured available electronically, Sybase IQ allow IT staff to group together upon the foundation and strengths of unstructured requiring text mining or compute resources, in a PlexQ grid, into the previous release. Sybase IQ has been analytics tool extraction or web data, virtual groups in order to isolate the designed to meet the growing needs of such as, social media – it simply doesn’t impact of different workloads and users IT and Business Analysts to tame the matter with Sybase IQ. from each other. When a user connects to a logical server and runs a query, the SAP Sybase IQ - Turning Big Data into a Big Advantage is published by Sybase. Editorial supplied by Sybase is independent of Gartner analysis. All Gartner research is © 2012 by Gartner, Inc. All rights reserved. All Gartner materials are used with Gartner’s permission. The use or publication of Gartner research does not indicate Gartner’s endorsement of Sybase’s products and/or strategies. Reproduction or distribution of this publication in any form without prior written permission is forbidden. The infor- mation contained herein has been obtained from sources believed to be reliable. Gartner disclaims all warranties as to the accuracy, completeness or adequacy of such information. Gartner shall have no liability for errors, omissions or inadequacies in the information contained herein or for interpretations thereof. The opinions expressed herein are subject to change without notice. Although Gartner research may include a discussion of related legal issues, Gartner does not provide legal advice or services and its research should not be construed or used as such. Gartner is a public company, and its shareholders may include firms and funds that have financial interests in enti- ties covered in Gartner research. Gartner’s Board of Directors may include senior managers of these firms or funds. Gartner research is produced independently by its research organization without input or influence from these firms, funds or their managers. For further information on the independence and integrity of Gartner research, see “Guiding Principles on Independence and Objectivity” on its website, http://www.gartner.com/technology/about/ombudsman/omb_guide2.jsp. 2
- 3. Figure 1: SAPSybase IQ - A complete and comprehensive big data analytics platform Source: Sybase query execution is only distributed to For statistics and data mining Sybase IQ techniques such as network analysis or for member nodes of the logical server, and supports a DBLytix library from Fuzzy searching large amounts of unstructured member nodes can be dynamically added Logix containing hundreds of advanced data that is not indexed. or dropped as necessary. analytic, statistical and data mining algorithms that can run inside Sybase IQ. In addition to a native MapReduce Specialized Tools & API, Sybase IQ offers four ways to Techniques For text analytics Sybase IQ provides integrate results from 3rd party Hadoop comprehensive in-database text search frameworks into Sybase IQ queries, giving Sybase IQ has partnered with a number capabilities. With Sybase IQ’s key a tiered approach to analyzing massive of key advanced analytic partners in Analytics partnerships – both internal and data sets. In essence, massive volumes order to provide key in-database analytics external, such as, SAP BusinessObjects, of data can be searched from distributed techniques. Using in-database analytics ISYS and KAPOW, hundreds of document file systems. The data returned from a enterprises and application vendors can formats and Web content can be ingested Hadoop analysis can then be integrated answer complex questions without having and/or extracted into Sybase IQ for into a Sybase IQ database in any of the to move mountains of data to 3rd party analysis. four ways: tools. With hundreds of statistical and • ETL Processing, which bulk load data mining techniques, advanced text Sybase IQ provides a native MapReduce data from Hadoop data stores into analytics capabilities, and APIs to execute API that can leverage massively parallel Sybase IQ using the open source proprietary algorithms safely inside processing across a PlexQ™ grid. Using utility SCOOP from Sybase’s Sybase IQ, companies can gain insights in MapReduce allows you to move beyond partner Cloudera. unparalleled time. limitations with SQL queries, enabling • Data Federation, which exposes you to more easily execute alternative HDFS files as tables in a Sybase IQ database that participate in SQL 3
- 4. queries (HDFS filesdo not need to to search and filter data for analysis in movement can impose severe constraints be loaded into Sybase IQ). combination with its column store engine. on timely delivery of the results you • Query Federation, allowing SQL Following the SQL Multimedia (SQL/ need to succeed and can account for up queries in Sybase IQ to execute MM) standard for storing and accessing to 75% of cycle time. By running analytic Hadoop processes that return data geospatial data, Sybase IQ supports 2D techniques inside the database Sybase that is incorporated into the SQL geometries in the form of points, curves IQ dramatically accelerates performance, result set, and finally. (line strings and strings of circular arcs), while avoiding governance and security and polygons. Sybase IQ also supports flat concerns caused by data movement. You • Client-side Federation, which and round-Earth representations, allowing need an analytics environment that can federates queries across Sybase you to choose the approach that best analyze large volumes of data from diverse IQ databases and Hadoop files addresses your situation. sources and provide fast, accurate results using the TOAD© SQL tool from - Sybase IQ’s in-database capabilities can Sybase’s partner Quest. Sybase IQ provides enterprises with APIs give you this advantage. to create proprietary analytic algorithms Use “R”, the popular open source that can run inside the Sybase IQ database Successful Analytics Platform statistical tool, to query Sybase IQ server for top performance. In particular, for Big Data databases using an RJDBC interface. Sybase IQ offers Java and C++ APIs, with Furthermore, you can execute R libraries these APIs you can create User Defined Sybase is on a mission to revolutionize Big from Sybase IQ as a function call within Functions (UDFs) that are called through Data Analytics with Sybase IQ. With our SQL queries and return result sets. SQL queries. The UDFs can access all of centralized data analysis delivering insights the data within a Sybase IQ database and across your Enterprise, and our support Sybase IQ also offers in-database can leverage a PlexQ™ grid for massively of large user communities running a wide execution of Predictive Model Markup parallel processing. Sybase IQ also offers range of analytics workloads – allowing Language (PMML) models through a an In-database analytics simulator, which organizations to analyze hundreds of certified plug-in from Zementis. This allows you to test a custom built UDF terabytes, even petabytes of data in allows you to automate the execution before deploying it into a production speeds up to 100 times faster – you can of analytic models defined using industry database. see that the big data challenges introduced standard language and that are created in SAS, SPSS Clementine, and other popular at the beginning of this article – volume, As you can see in-database analytics is a velocity, variety, costs and skills, are predictive workbench products. By using key component to Sybase IQ’s success in matched with the growing set features industry standard languages it enables being an advanced analytics platform for and capabilities offered by SAP Sybase IQ. you to leverage your existing investments Big Data. Data volume, accuracy, and swift Now with accurate complete information while providing better performance and processing time are all factors critical for across your enterprise, Big Data doesn’t scalability. success but the balancing act between seem like such a Big Problem it has turned these key components continues to pose into a Big Advantage – with SAP Sybase Within Sybase IQ, the row store SQL serious challenges for most organizations. IQ! Anywhere engine, allows you to also With traditional analytics this data create indexes of geospatial information Source: Sybase 4
- 5. Gartner Reserch: MagicQuadrant for Data Warehouse Database Management Systems The data warehouse DBMS market used as a data warehouse – rather, a data data (SSED), excluding all data warehouse is undergoing a transformation with warehouse (solution/data architecture) design-specific structures (such as indexes, the introduction of “big data” and the is deployed on a DBMS platform. A data cubes, stars and summary tables). SSED logical data warehouse demand for new warehouse solution architecture can is the actual row/byte count of data techniques in practices and technology. and often does, use many different data extracted from all sources. The integration of professional services constructs and repositories. Importantly, From 2012 onwards, defining the size of with product offerings also increased in the definition of this market is changing a warehouse will become less important importance in 2011. and a DBMS will become only part of the and information asset access will become overall market definition as the logical more important. Within SSED it is Market Definition/Description data warehouse (LDW) continues to important to separate the actual data size This document was revised on 05 March grow in acceptance and deployment. in a data warehouse from the database 2012. The document you are viewing total size. Gartner clients report that is the corrected version. For more A data warehouse is a database in which many 100-terabyte warehouses often information, see the Corrections page on two or more disparate data sources can hold less than 30 terabytes of actual data. gartner.com. be brought together in an integrated, Throughout 2012 and 2013, the size of a time-variant information management warehouse will evolve toward a combined The supplier side of the data warehouse strategy. Its logical design includes the metric, relative to the repositories under database management system (DBMS) flexibility to introduce additional disparate direct management of the warehouse and market consists of those vendors data without significant modification complemented by the volume of available supplying DBMS products for the database of any existing entity design. A data information accessed by the warehouse, infrastructure of a data warehouse and warehouse DBMS is now expected to as well as its performance in doing so (see the required operational management coordinate virtualization strategies, as Note 3). controls. well as distributed and/or processing approaches such as MapReduce, to In addition, for the purposes of this For the purposes of this Magic Quadrant handle one aspect of big or extreme data analysis, we treat all of a vendor’s analysis, a DBMS is defined as a complete situations. products as a set. If a vendor markets software system that supports and more than one DBMS that can be used manages a logical database or databases A data warehouse can be of any size. The as a data warehouse DBMS, we note in storage. Data warehouse DBMSs are sizing definitions of traditional warehouses this fact in the section related to the systems that, in addition to supporting remain as: specific vendor, but evaluate its products the relational data model (extended to • Small data warehouses are less than together as a single entity. Further, a support new structures and data types 5 TB. DBMS product must be part of a vendor’s such as materialized views, XML and product set for the majority of the • Midsize data warehouses are 5 TB metadata-enabled access to content), calendar year in question. If a product to 20 TB. support data availability to independent or vendor is acquired mid-year, it will be front-end application software and • Large data warehouse are greater labeled appropriately but placed separately include mechanisms to isolate workload than 20 TB on the Magic Quadrant until the following requirements (see Note 2) and control year (see Figure 1). various parameters of end-user access Importantly, none of these categories within a single instance of the data. qualify a warehouse as a “big data” There are many different delivery models, warehouse. Volume alone is not “Big such as stand-alone DBMS software, This market is specific to DBMSs used data.” For the purpose of measuring the certified configurations, data warehouse as a platform for a data warehouse. It is size of a data warehouse database, we appliances (see Note 1) and cloud (public important to note that a DBMS cannot be define data as source-system-extracted and private) offerings. These are also evaluated together within the analysis of each vendor. 5
- 6. Figure 1. MagicQuadrant for Data Warehouse Database Management Systems is either a visionary with cloud and data warehouse as a service, but does not execute against the challengers leaders rest of the market, or it is good at execution against two of the many use cases in the market with little Teradata vision for the remainder. Oracle The 1010data position is almost IBM perpendicular to our combined EMC/Greenplum evaluation criteria. Therefore, we ability to execute Sybase, an SAP Company have placed it with high execution 1010data against a sub-section of the market Microsoft we evaluate. From a visionary ParAccel Vertica perspective, 1010data is difficult Kognitio to evaluate under current criteria. SAND Technology Its approach in using a cloud- Infobright based and “as a service” DBMS/ analytics solution is the primary Actian business model and technology approach. Cloud-based analytics as a service and the ability to deliver Exasol under a managed on-premises niche players visionaries model, leaves 1010data short of the much broader vision desired completeness of vision by the greatest portion of the As of February 2012 data warehouse market, but in these few delivery segments of the Source: Gartner (February 2012) market 1010data is a formidable performance competitor. • 1010data is expected to add Magic Quadrant share large amounts of data without probabilistic matching in 2012. needing to manage it locally – for The company has exhibited Vendor Strengths and Cautions example, large quantities of CPG significantly more reduced load 1010data times than some of its significant data can be shared by multiple retail 1010data (www.1010data.com) was big data competitors, as well as companies. established 11 years ago as a managed orders of magnitude and faster service data warehouse provider with an As a managed service solution performance in extremely large integrated DBMS and business intelligence vendor, 1010data can complement datasets. 1010data products read (BI) solution primarily for the financial the customer’s internal IT SQL, but also utilize their own, sector and more recently, the retail/ department with fast-to-market non-SQL language that performs consumer packaged goods (CPG) sector. solutions for business units, so high-speed joins with unplanned 1010data can host its solution using reducing resource consumption data rationalization built into the traditional software as a service (SaaS) within the IT department. More queries without the performance model or support a managed solution importantly, the managed service disadvantages of using interim at the customer’s site. 1010data has model enables 1010data to leverage return datasets. approximately 200 customers. software solutions across multiple customers. As new applications are • Perhaps the most important Strengths created, they become available to point raised by those customers all clients, increasing the availability referenced is that 1010data is • Since 1010data offers a complete utilized by both IT and the business of these applications to businesses. SaaS solution, the customer’s with fast response times on queries With more than 200 customers, business unit and IT organization running against hundreds of billions 1010data has reached a position to need little experience of data of row tables (with a combined break out of its former niche status. warehousing or BI. The SaaS model number of rows throughout The problem is that the company also allows multiple organizations to 6
- 7. databases exceeding atrillion rows As the demand for hybrid analytics Actian in the entire database in some mixing structured data with content Actian (www.actian.com) offers two instances). The company also increases, 1010data will need to products, the general-purpose Ingres serves as a data aggregator and data introduce unstructured data analysis DBMS and Vectorwise, a new offering marketplace providing datasets for as well as operational technology introduced in June 2010 and targeted at rapid enhancement and enrichment or machine-generated data analysis. analytic data warehouses. Open-source of analytics normally bound to 1010data’s competitors have greater Ingres, one of the original RDBMS internal datasets only. financial resources and already are engines, has a 30-year history and claims Our reference checks and in the process of building out this more than 10,000 customers running discussions with Gartner clients part of the data warehouse vision. mission-critical applications, including data also show that 1010data is • One of 1010data’s strengths warehouses. price-competitive with non-SaaS also acts as a caution. While the alternatives, especially by reducing business prefers a solution that is a Strengths the management overheads needed complete, deployment-ready stack, • The Actian database contains most to support a data warehouse IT departments and purchasing of the features necessary for data environment. 1010data has offices do not. 1010data’s offering is warehousing, such as partitioning, expanded from the financial sector sold as a fully integrated DBMS and compression, parallel querying (where it began) into a broader BI solution, which limits potential and multidimensional structures. market, including the retail sector. customers to those wanting a Release 10 added bulk load, scalar 1010data now claims more than full solution (primarily because subqueries, long identifiers and 200 customers and its customer of 1010data’s pricing model). a geospatial offering that was references support our belief that 1010data’s product is a compliant, community driven with hundreds it is one of the stronger small relational DBMS (RDBMS) that of committers contributing code. data warehouse DBMS vendors. customers can use as a stand-alone The performance of Vectorwise, In addition, the company has a system if desired – but fees are especially in analytic applications, small number of customers that charged as if the entire solution is was cited by customers interviewed install its system on-premises as managed. Customers are advised to by Gartner. With the emergence a managed solution, with several check the total cost of ownership of new server platforms with using 1010data as an enterprise in such cases, as it may not be storage-class memory (of 1 TB and data warehouse solution vendor. advantageous to use 1010data in more), Vectorwise will prove a Therefore, from an execution this way. valuable asset for data warehousing standpoint, 1010data matches • As a solution vendor, 1010data and analytics as more of the data performance, pricing and delivery has a different competitive warehouse moves to memory. model for two specific needs in model from vendors of pure-play • Actian has aggressively pursued the market quite well and it is DBMS offerings. In addition to partners, including independent expanding both its scope of delivery competing in the data warehouse software vendors (ISVs) in the BI and its vertical customer base. DBMS market, it competes with market, the primary driver of new system integration vendors that installations in data warehousing. Cautions offer outsourced solutions, such Both new and existing customers • The market continues to resist as Cognizant and HP (via EDS). are looking for an open-source fully-managed data warehouse Additionally, IBM, Oracle and other BI stack with partners such as services in many verticals and large vendors with professional Jaspersoft and commercial BI horizontal use cases. 1010data is service organizations compete with vendors such as MicroStrategy susceptible to resistance from IT 1010data in two markets, data have also engaged with Actian. departments requiring all its data warehouse DBMSs and services. It Ingres and Vectorwise are gaining warehouses to be located in-house, remains to be seen if this is a bias attention from vertical application along with in-house governance to be overcome or if the cloud vendors, system integrators and of the organization’s data assets. and on-premises mix will ultimately resellers. Vectorwise uses some The IT market is not fickle and exclude a vendor like 1010data. Ingres software atop a column store persists in its use of better name- However, based on its extremely from the MonetDB project and uses branded vendors and not simply positive customer references, it hardware assists, turning columns because they are name-branded. is very unlikely 1010data will be into vectors and processing them excluded from such a mix. in x86 chip registers to leverage 7
- 8. instruction parallelism andon-chip • Actian offers professional services Strengths caching. Vectorwise has delivered in data warehousing and has a go- • Greenplum’s understanding and several top non-clustered TPC-H to-market strategy with a growing vision of the data warehouse benchmark results at 1 TB and stable of partners – it claims half market was ahead of the market as below. The company was renamed of its 2011 Vectorwise sales have it was one of the first to work with in late 2011 and introduced another come though channels. However, MapReduce, manage external files new product offering, the Cloud it lacks data models and must from within the DBMS and optimize Action Platform, to support the continue to add marketing and sales for very large database sizes. As delivery of “Action Apps” that expertise for data warehousing. big data is now important in the will act on the analytic capabilities Additionally, Actian has strength market and the LDW is emerging as Actian supports. in open-source, but the overall a necessary functionality to support • Previous reference checks have adoption of open-source for data today’s mix of volume, velocity, shown Ingres customers to be very warehousing remains weak. While variety and complexity, Greenplum loyal. Most have online transaction Actian has professional services, it has a base to support this that was processing (OLTP) applications, tends to lack some of the tools and launched several years ago, which but Ingres has also been used methodology support that other translates into the high ability to for smaller data warehouses organizations have readily available. execute. (historically up to about 2 TB, the • Actian’s new brand and name, as Greenplum announced the first company is targeting warehouses well as its portfolio expansions, can unified analytics appliance addressing smaller than 10 TB). Among open- help overcome Ingres’s reputation big data (a modular solution for source DBMS, only Oracle’s MySQL as an older product that has not structured and unstructured data), compares with proven maturity regained much market traction. in May 2011 that was released for mission-critical applications, Importantly, Actian has taken a bold in September 2011. The EMC including data warehousing. stance in attempting to re-establish Greenplum Data Computing Vectorwise has begun to gain new itself with a new vision and new Appliance (DCA) uses the customers and software partners, plans for execution. Initial response Greenplum Database, Greenplum targeting another set of use cases. to Vectorwise is significant with the HD (Hadoop), and Greenplum Now in its version 2.0, it has added addition of more than 20 customers Data Integration Accelerator (DIA) Windows as a platform and has a in its first year offering and users modules that can be configured clear road map for several future should consider Actian’s Vectorwise within one single appliance cluster. releases. to be a new and innovative In addition, Greenplum has Chorus, solution in that respect. However, its analytics productivity software, Cautions market perception is difficult to leveraging VMware’s technology, to change. Both offerings have gained support automated, self-service data • Although Vectorwise enhances new customers and third-party services and collaborative analytics. Actian’s ability to support analytic relationships, but to become a In a recent announcement, EMC data marts, the company must serious competitor in this market announced the first Hadoop NAS continue to address enhanced data Actian must continue to show attached HDFS system – HDFS warehouse functionality, storage increased growth in both revenue running native on EMC Isilon management and mixed workload and numbers of new customers at connected to the Greenplum HD management if it is to compete a higher rate than it has thus far. or Greenplum Data Computing with larger, equally mature vendors Effective marketing execution is a Appliance (DCA). Finally, through and meet the needs of the broader must-have for Actian to compete. the external file mechanisms and data warehouse DBMS market. Vectorwise needs to support more user defined functions (UDF), analytic SQL constructs than it does EMC/Greenplum Greenplum has started along the now and add stored procedures Greenplum (www.greenplum.com) is part path to support LDW. Greenplum and user-defined functions and of the Data Products division of EMC even supports an iOS, Linux and data types to move closer to with a massively parallel processing (MPP) Windows single-user development competitors. Its new product and data warehouse DBMS running on Linux system downloadable as free (not restructuring around Action Apps and Unix. It can be sold as an appliance or open-source) software. can be synergistic – but could also as a stand-alone DBMS and has more than • As Greenplum has settled into prove distracting. 400 customers worldwide. the EMC organization, we have 8
- 9. seen an increasein hiring directly presence to compete with all the Exasol related to development. This, incumbent, large DBMS vendors. Exasol (www.exasol.com) is a small DBMS coupled with the EMC development Importantly, EMC’s customer base vendor in Nuremberg, Germany. Exasol organization has led Greenplum is primarily within the IT unit of has been in business since 2000 with the to offer its DCA supporting big the organization. Data warehousing first in-memory column-store DBMS, data for both structured and is the technical infrastructure for EXASolution, available since 2004 and unstructured data and intergraded an intensely business-oriented primarily used as a data mart for analytic MapReduce processing. The DCA use-case. EMC will need to learn applications. is now assembled by EMC and sold from its Greenplum acquired by its sales force. In an interesting knowledgebase, specifically how to Strengths manufacturing cost management solution sell a data warehouse and • Exasol offers an in-memory column- model, EMC is assembling its analytics solution. store DBMS for data warehousing. appliances in different countries • Interestingly, this year our customer As we have stated, this technology around the world, affording EMC references have raised several is one of the critical capabilities of Greenplum a tax advantage in many issues around support. In these the future for the data warehouse countries where others (such as cases it was not related to the DBMS market. Exasol runs in a Oracle and Teradata) are subject attention to rapid support and clustered environment offering to stiff import duties. This positions fixes (with all customers stating scalability across multiple servers. the company for easier entry fixes were available in an expected, Not only does this allow for high- into global markets. Due to the timely manner), but more with availability in the case of a server acquisition, Greenplum has been the bugs in the first place. We failure using EXACluster OS, but able to work more closely with would classify these as “growing also scaling for larger memory sizes. VMware, for example rearchitecting pains” especially for a small EXASolution maintains redundant the Chorus private cloud offering. organization (as Greenplum was copies of the data in memory to • Our customer references support pre-acquisition) being integrated reduce the downtime associated the claims of high performance into a large organization such as with server failures. as well as advantageous price/ EMC. We should also note that in Exasol also includes the use of disk performance ratios. These our inquiries with Gartner clients, for persistence and overflow (if all references also support the we have seen this issue diminish, the data does not fit in memory). Greenplum claim of scalability to coupled with consistently high However, when data is loaded into very large database sizes. Reported marks for personalized customer Exasol, it is loaded into memory sizes range from 10 terabytes to support. first and then written to the disk, more than 500 terabytes. When • As Greenplum leverages EMC allowing for the applications to this combination of performance more, it will find itself competing begin before the slower activity and scalability are joined to an at a higher level with the mature, of disk input/output (I/O) is appliance, the potential of EMC/ incumbent vendors. The major completed. This separation of the Greenplum to compete in the data vendors (such as IBM, Oracle, SAP data access and data persistence warehouse market is increased. and Teradata), have a much larger model is a visionary change for the customer base allowing them, as market. Additionally, as a column- Cautions the incumbent, a stronger position. store, Exasol has excellent data • Although acquired by EMC 18 EMC/Greenplum must continue compression (reported to be on months ago and despite doubling to demonstrate differentiation as average, four times faster), thus the install-base, Greenplum’s it addresses the data warehouse reducing the amount of memory market position is sixth or seventh market and big data is one specific necessary. EXASolution is sold by worldwide. To really increase area, as is cloud. The company must the amount of memory used for the velocity and gain market share, continue to support customers data. Greenplum must continue to accustomed to the type of service • Another advantage of Exasol, as develop the EMC sales force so provided by a small company with other in-memory DBMSs, is that it has the necessary skills with focused, customer-specific the high speed of the DBMS. In in the DBMS software market. professional services solutions, published benchmarks, Exasol has Greenplum must also continue issue-focused support and leveraging attained data warehouse transaction to leverage the EMC worldwide key customer inputs for product speeds up to 20 times the closest enhancements. 9
- 10. competitor. Server memory Exasol lacked a marketing vision vendors such as Quest are less is expensive, but these same to grow beyond the borders of its likely to support the DBMS, benchmarks demonstrated costs European base. The company began requiring Exasol to create their own of approximately one-third of the an expansion plan in 2011 and management software. standard DBMS. Our reference will begin to grow offices in other checks also validate the claims of locations, including North America. IBM cost reduction and speed. Another • Another issue is the increasing IBM (www.ibm.com) offers stand- strength of the in-memory nature competition, both in column-store alone DBMS solutions as well as data of Exasol is removing the necessity and in-memory. Exasol has a clear warehouse appliances, currently marketed of optimization and calculation advantage being the first with an as the IBM Smart Analytics System family structures within the database. in-memory column-store DBMS. (ISAS) and the Netezza brand. IBM’s There is no need to build Now, most of the DBMS vendors data warehouse software, InfoSphere summaries, aggregates and cubes offer some form of column-store Warehouse, is available on Unix, Linux, for use in business intelligence capabilities. Further, when Exasol Windows and z/OS. IBM has also and analytics. This reduces the began, there were only a handful of continued research and development and overhead in the DBMS by as much in-memory DBMS, mostly used for market execution for the Netezza brand as 10 times, as well as reducing streaming data applications. There and product line following its acquisition. the database administrator (DBA) are now many in-memory DBMSs IBM has thousands of database customers resources used to maintain such available in both the column and worldwide and more than 500 appliance structures. In addition, this also row-store variety. Finally, SAP has customers (Netezza and ISAS combined). leads to very fast load times, released its SAP HANA appliance as there are no complicated with an in-memory column-store Strengths structures to build during loading. DBMS for an analytics data mart • The breadth of IBM technology • Customer references clearly and now available under the SAP offerings is complementary to espouse the abilities of NetWeaver Business Warehouse. and part of its solution delivery EXASolution for both pure As with many technologies, capability. InfoSphere Warehouse, performance and cost/performance. being first is not sufficient unless a data warehouse offering based The references (although few in capitalized in growth of market on IBM DB2, is a software-only number) also state that customer share. Exasol has missed the solution. IBM’s data warehouse support is excellent. Finally, window of opportunity of being appliance solution, the IBM references corroborate the results first and now faces increased Smart Analytics System (ISAS) is of the benchmarks mentioned competition. a combined server and storage here, with better than 20 times • Customer references report that hardware solution (using the IBM performance at half to a third there is one major issue with the Power Systems server with AIX, of the cost. They also support use of EXASolution – the lack of the System x server with Linux or the claims of 4 times (or more) interfaces to common BI tools. Windows and the IBM InfoSphere compression. Exasol offers the standard ODBC Warehouse and a robust System and JDBC interfaces, but this can z ISAS data warehouse solution), Cautions be a performance drawback with complete with service and support. tools such as BusinessObjects, • The primary challenge Exasol faces IBM’s introduction of InfoSphere Cognos and SAS. As Exasol has a is the small size of the company and BigInsights includes offerings to aid small installed base, it is difficult to previous lack of expansion beyond the design, installation, integration engage the tools vendors to assist Germany. Exasol was primarily and monitoring of the use of in creating native interfaces to the engaged in product development Hadoop technologies within an DBMS. We do expect to see this for its first five years of operations IBM-supported environment. In remedied over the next few years and with changes in management IBM’s case, it is important to note as the size of the installed base two years ago has now obtained that it has embraced the vision grows. Similarly, there is a reported the vast majority of its 30 or more for the LDW – which Gartner lack of software to manage the customer base in the past two describes as the emerging new best Exasol environment (EXASolution). years. These customers are mostly practices in analytics management. Again, with a small installed base, located in Germany, with several in By tying together relational data, third-party management software Italy and Japan. Until very recently, data streams and Hadoop files, 10
- 11. IBM’s stack buildsconfidence among IBM specifically assigns technical own methodology and highlights managers of existing warehouse account managers to support that the traditional enterprise data implementations that the product is accounts). Additionally, IBM’s focus warehouse [EDW] is vital to all data evolving as new demands for these on prospect qualification resulted in warehouse strategies including as a two components of the logical data a higher growth in 2011 vs. 2009 to base component for the LDW. warehouse emerge. 2010 for all of its products. This was IBM’s first incarnation of Additionally, for Smart • The overall effect is that referenced the LDW approach. The market Consolidation – rather than customers are confident regarding is acknowledging that the EDW developing tooling in isolation, IBM release dates and the road map. does not have to be the center of focused on tooling that existed in Customers list concurrency, the strategy but will be significant. its Information Integration portfolio scalability, performance optimization However, the justification for (InfoSphere BluePrint Director). and support as positives and were the LDW and evolving existing This resulted in improvements in the most often repeated phrases warehouses or replacing them the area of integration, including but in the reference survey in 2011. will be difficult at first because not limited to the common Data References elaborated by indicating it appears to supporters of Warehouse Packs and Models now that partitioning, compression and traditional data warehouses to supported on DB2 and Netezza reduced administrative hours all be a radical departure from their platforms alike. contribute to their experience to beloved traditional data warehouse • IBM combines product sales with support optimized performance. practices. Gartner’s own research solution services. This market At the same time, some references indicates that the LDW approach is demands a widely varied level reported that optimization of quickly emerging as the newest data of sophistication and knowledge queries should be targeted rather warehouse best practice. Gartner depending on each client than being forced to optimize every anticipates the LDW will become organization’s maturity in analytics single query because the system is a best practices approach during and information management. As able to engage a solid query plan for 2013-2015. With market leadership noted in the overview, the data execution. This evaluation considers there is risk commensurate with the warehouse market in 2011 has the LDW concept to be innovative, anticipated rewards. IBM will need multiple visions for the future. but has yet to see a wider embrace to continue their careful education IBM has embraced the logical in the market. IBM’s early adoption message regarding their leadership data warehouse (via “Smart of the LDW concept in both its approach in LDW practices. When Consolidation”) approach while messaging and its product road engaging in an LDW approach continuing to advance its technology map has established this vendor as with IBM, clients should insure solutions and implementation an early resource for the market. they completely understand IBM’s practices supporting traditional data However, the majority of the positioning for implementing this warehousing architectures. market for data warehousing will solution. Professional services available remain significantly focused on • Gartner inquiries report indicate from IBM range from expert traditional solutions for a minimum that IBM data warehouse solutions education through turnkey of the next three years. are also marketed and delivered in solutions to managed services for isolation from each other. There are data warehousing. Importantly, Cautions strategic reasons to continue such where IBM leverages its services an approach with any acquisition, • IBM has embraced the logical data organization most, is in feeding but Netezza products tend to have warehouse vision as the likely field experiences into the overall their own niche in customers’ minds successor to current best practices data warehouse vision. In 2010, that is viewed as being separate and in traditional data warehousing. The clients reported that IBM’s support distinct from IBM (but Netezza’s market has not yet determined if appears disconnected from its growth was more than 30% in 2011, it is ready to adopt this approach product strategy – this improved in which is faster than its previous as the new vision for the data 2011 with an even larger reference growth rate as an independent warehouse and abandon 20 years base reporting. This does not mean company). of traditional best practices. the issue has been resolved, but it IBM’s professional services have As a result, IBM customers often appears that IBM’s focus on solution experience in delivering various engage only part of the organization services is paying off (for example, aspects of the LDW under its for solutions and at least in the 11
- 12. customer’s minds, eliminatethe compressed DBMS. The company Infobright also released an option others. This creates both marketing provides both an open-source version for the Enterprise Edition called the and sales process challenges. This (Infobright Community Edition [ICE]) Distributed Load Processor (DLP) is not an issue with shortlisted and a commercial version (Infobright which allows for the parallel loading solutions (IBM should recommend Enterprise Edition [IEE]). Infobright has of data into the system at very high one solution or another), but does approximately 200 customers worldwide. speeds. Infobright has also added carry over into the solution delivery connectivity to Hadoop MapReduce team and IBM is missing some Strengths for the processing of “Big data.” opportunities for the different parts • Infobright remains one of the only This is extremely important to of the sales organization to leverage column-store DBMS in the open- the machine-generated data world each other. IBM has implemented source software environment. as much of this data is stored in organizational changes intended to Its revenue is generated from Hadoop or other such file systems address these issues. the Enterprise Edition (using a and needs to be extracted into a Netezza and IBM personnel do commercial license, rather than a DBMS for processing. interact and coordinate with General Public License [GPL]) with • Our customer references are clear each other behind the scenes. a subscription support model based on several points. Infobright is A marketing solution would on the amount of SSED stored in extremely fast compared to other simply begin branding software the system. As we stated in 2011, systems, including MySQL. Reports and hardware combinations for Infobright decided in mid-2010 to of up to an average 500% increase limited purposes. However, IBM focus on operational technology in performance over MySQL will choose the more difficult (and data (which it calls machine- deployments have been reported. more appropriate) solution of generated data). This encompasses We believe this is not only from creating an educational sales and data from sources such as smart the column-store design, but also implementation process which meter data (in the utilities space), the Knowledge Grid. References will demonstrate how software customer data records (in the telco suggest that Infobright is replacing and hardware capabilities can be space) and clickstream data from an existing MySQL environment leveraged effectively to support Internet interactions. with great gains in stability, each use case. This focus has helped Infobright compression and performance. • IBM customers report (via inquiry during 2011 where its customer Some cases report a year or more and reference survey results) base has grown to more than 200 without an outage. a scattering of intermittent direct and OEM channel customers. Finally, many references state that and irregular issues with Not only has this focus increased simplicity is a factor in their choice product performance or their customers, but has also attracted to use Infobright. We also believe implementation experience. Some a number of additional OEMs this will interest OEMs that want to of these are possibly attributed to (now accounting for approximately build-in Infobright to their existing the implementation process and 40% of customers). This, along systems for resale. The simplicity not the products. However, these with partnerships with Pentaho, of management, scalability and same customers report that IBM Jaspersoft, Talend and others, will compression all interest the OEM support addresses these issues with help the company grow substantially looking for a DBMS to embed that efficiency. Nonetheless, as with faster than direct sales only. requires little support on their part. any IT products, an assumption • Infobright has several unique The focus on machine-generated that appliances or certified technologies in the DBMS. In data has been important to configurations alleviate all issues is addition to the column-store file Infobright, but we believe that the incorrect. Most issues are irregular system for MySQL, the Knowledge future will greatly depend on the in nature and IBM support is Grid in-memory metadata store company’s ability to leverage these intimately involved in the resolution is a major differentiator for OEM partners. process. Infobright, as this product analyzes queries to minimize the number Cautions Infobright of “data packs” that have to be • One of the biggest challenges for Infobright (www.infobright.com) has decompressed to give a result (data a small vendor is to focus on what offices in Canada, Europe and the packs are the compressed domains/ they do well. Infobright has done U.S. and offers a combination of a regions of data in Infobright’s this with machine-generated data. column-vectored DBMS and a fully offering). 12
- 13. However, as asmall, relatively MySQL. To date, Oracle has not started to produce results, with young vendor, Infobright must done anything other than enhance several new customers. Kognitio continue to differentiate its the product. However, in the future has also added several hosting offerings and open-source model when the contract is done with EU, partners in the U.S. and the U.K. from mature column-store DBMSs. we cannot guarantee that Oracle offering managed services on WX2. Sometimes, these two statements will not change the agreements, Its sales model as dbSaaS makes up are contradictory not least because especially those with OEMs. This almost half of its revenue and has the focus on machine-generated is an issue customers of Infobright supported much of the company’s data cannot be an excuse for should monitor in the future. growth this year. ignoring its existing customers • Kognitio continues to invest in addressing other data management in-memory capabilities. Gartner Kognitio use cases, reported in several considers that in-memory DBMSs Kognitio (www.kognitio.com) started by customer references as an issue. An can play a major role in enterprises offering data warehouse appliances and example is workload management information infrastructure and as warehousing as a hosted service. Today, software, where the managed such Kognitio’s technology has it has a mixture of less than 50 customers workloads are basically for machine- an opportunity to meet customer using its DBMS (WX2) separately as an generated data and may lack the demand, given the maturity of its appliance, a data warehouse DBMS engine, robustness needed for management offering, compared to other more or data warehousing as a managed service of overall workload. recent offerings. Kognitio’s DBMS, (hosted on hardware located at Kognitio’s • There are other issues raised by sites or those of its partners). WX2 version 7, already includes our reference checks. As with most in-memory analytics, and customer small startup vendors, stability from Strengths references continue to report one release to another can suffer. that the speed of query and load • Kognitio pioneered the data Customer references reveal that performance is excellent. In 2011, warehousing database as a service there have been issues with new Kognitio added Pablo in-memory (dbSaaS) model, where a data releases, but they are quick to point online analytical processing (OLAP) warehouse DBMS is delivered out that the problems are quickly capabilities to further strengthen its as a managed service from the resolved. The lack of management analytical capabilities The DBMS is DBMS vendor. Clients buy data software (also an issue for smaller already an in-memory DBMS, with warehousing services from Kognitio, vendors) was raised. Third-party hot data held in-memory and cold while Kognitio hosts the database. software vendors are not quick data on disk, managed automatically Data warehousing dbSaaS permits to pick up new, young software by the DBMS. clients to expand their warehouses companies, as the potential market • Those customers referenced incrementally and clients note is small, so this puts more pressure reported significant concurrency that this model provides for low on Infobright to produce its own capabilities, as well as excellent upfront costs with virtually no management software. support and product management. capital expenditure required to • Finally, Infobright is open-source get started. This is a growing Kognitio is gaining visibility thanks and makes use of portions of segment of the data warehouse to the current market interest in MySQL, under a Commercial OEM DBMS market. Kognitio also works in-memory technologies. Kognitio’s License with Oracle. We always with deployment partners such customers report that deployment question the open-source model as Capgemini (and contributes of large-scale data warehouse for revenue generation. First, to Capgemini’s Immediate cloud efforts takes as little as 10 weeks Infobright has a community version computing offering). using this model. References also with less functionality than the report predictable, linear scaling of Additionally, in line with existing Enterprise Edition. This has proven performance and under the “as a market demands, Kognitio has useful as a trial system to attract service” model, customers report an appliance to install on-site for new customers, but some may opt scale up and scale down needs as customers requiring their own for the ICE version in lieu of the part of a solid account management infrastructures. Kognitio opened Enterprise Edition. approach. Finally and possibly most offices in the U.S. three years ago The other issue is specifically the importantly, references indicate that in addition to its U.K. headquarters use of MySQL, as it is owned by new queries and new variations on and has continued to expand its Oracle. This implies risks remain existing analytics can be deployed presence in the U.S. by hiring due to the uncertain future of rapidly. additional resources. This has 13
- 14. Cautions such as those of IBM (Cognos) can also leverage SharePoint and • Kognitio has a very substantial and SAP (BusinessObjects), is PowerPivot and the ability to opportunity in the small or midsize difficult to manage. This problem include an unstructured information business data warehouse and is compounded by Kognitio’s type in analytics is the result of BI market thanks to its dbSaaS small market penetration and the its technology blend and this is a model. However, over the past resulting scarcity of tool expertise strength that should definitely not year, managed services offerings in the market. References also be ignored. from IBM and HP/Vertica have report the absence of any form of • References report that Microsoft experienced growing acceptance developers’ forum or marketplace, exhibits one of the best value and penetration in the market. scarcity of skills in the market and propositions on the market with These offerings are not direct an extremely lean global presence a low cost and a highly favorable competitors to Kognitio’s solution, makes commitment to the product price/performance ratio. Skills are but the customer base views them and consistent delivery difficult. widely available in the marketplace as an equal alternative from more to operate a Microsoft data established vendors. Microsoft warehouse and there is an easy Kognitio has not yet addressed Microsoft (www.microsoft.com) continues learning curve to acquire those some of the very large volume to market its SQL Server 2008 DBMS same skills, as needed. As an added or variety of data support issues (Release 2) Business Data Warehouse bonus, customers report that the – more specifically support for and Fast Track Data Warehouse for data integration and continuity of a content and complexity aspects of warehousing customers not requiring an complete Microsoft data warehouse extreme information. However, MPP DBMS. Microsoft released its own and business intelligence stack is Kognitio’s in-memory analytical MPP data warehouse appliance, the SQL highly advantageous to time-to-value capabilities can be of value in low Server 2008 R2 Parallel Data Warehouse in delivery. Noticeably absent are latency, high volume analytics. (Microsoft) (PDW), in November 2010. any fears regarding vendor lock-in. The market shifted dramatically Strengths According to our reference checks during 2011 toward a new position. and discussions with our clients, • Microsoft spent 2011 revitalizing Kognitio did not stand still, but worldwide support from Microsoft its vision for the data warehouse market demand regarding new is extensive, encompassing partners, market. Additionally, it announced functionality expanded more rapidly value-added re-sellers, vendors of two Apache/Hadoop connectors than Kognitio’s product feature third-party software and tools and for SQL Server, SMP and Parallel sets. This appears to only be a widely available SQL Server skills. Data Warehouse (PDW) in temporary condition while Kognitio support of the market’s big data • Microsoft references indicate a addresses these new expectations. issues. Many would be surprised dominant presence in midsize data • While Kognitio continues to grow to learn that Microsoft already warehouses —especially those its installed base (with an additional provided combined structured end-user organizations reporting seven clients in 2011) the company and unstructured analysis in SQL that their companies and their data remains a small vendor with fewer Server 2008/R2. A third quarter management needs are growing. than 50 customers worldwide. appliance update included support According to customer references, This makes it increasingly difficult and enhancements for integration Microsoft assures its customers of to sell to organizations that have with SAP/Business Objects, a solid data warehouse platform incumbent vendors, and to compete MicroStrategy and Informatica. including features and functions with some of the lower-priced that run the gamut of traditional In addition, Microsoft offers the appliance offerings. Additionally, warehouse functionality. SQL Server Fast Track Data as a data warehouse outsourcing Warehouse, which includes For connectivity in a multi- solution, organizations should be validated reference architectures vendor environment Microsoft aware that they are still responsible for building a balanced data offers a SAP/BW, Teradata and for contracting and auditing data warehouse infrastructure. This Oracle connector. The DBMS security procedures. road map contributes significantly supports compression and • Clients report interoperability to the company’s vision for the backup compression, partitioned with third-party popular BI tools, market and its customers. Microsoft table parallelism, policy-based 14
- 15. administration and evenstar-join weaknesses cited by references, Oracle query optimization. Microsoft Microsoft offers all of the “parts” Oracle (www.oracle.com) offers a choice also offers analytics capability, of a solution, but it is difficult to of products, which allow customers to coordinated through its data assemble and use those parts out of choose to build a custom warehouse, use warehouse products, to perform the box. a certified configuration or purchase an hybrid analytics, which combine Nevertheless, the strength of appliance ready for a warehouse design data and content —representing performance/price remains to and load. In addition to the DBMS and an area of important vision in the balance these issues. Microsoft certified configurations, Oracle offers logical data warehouse space. maintains these issues are mitigated three different Exadata branded products: by the Reference Architectures in Oracle Exadata X2-2 for data warehousing Cautions Fast Track (which does receive high and mixed workloads, Oracle Exadata • As of the completion of our praise in the market) and appliances X2-8 for cloud solutions and Oracle research for this Magic Quadrant such as Parallel Data Warehouse Exadata Storage Expansion Rack X2-2 for analysis (November 2011), and Business Data Warehouse. additional storage capacity. Oracle reports Microsoft could not provide a • It is important to note that more than 300,000 customers worldwide. production reference for the PDW Microsoft’s highly improved vision – but does have paying customers. now needs to come to fruition. We Strengths Last year our view, while late, have seen other vendors bounce • The Oracle DBMS versions was that the PDW was arriving back and forth between focusing represent approximately 43% of just in time for later adopters on market execution and product the total DBMS market share in the data warehouse appliance and market vision. If this type of (not just warehouses) by revenue market. This window is not closing bouncing is the result of revitalizing worldwide. Customer references anytime soon, but credibility product and solution delivery and report a tendency to continue to needs to be established and some simply working out the “kinks,” use Oracle’s DBMS and deploy issues continue to be reported then the vendor is usually successful more applications on the database. by references. The difficulty with in achieving higher success in the Further, while there is no accurate high availability using active-passive marketplace. However, we have representation of data warehouse server clustering and a relative lack also seen this phenomenon in market share (DBMS licenses can be of performance-monitoring tools vendors with inconsistent data used for multiple purposes), Oracle specifically related to SQL Server warehouse product leadership. announced that 1,000 Oracle Integration Services (Microsoft) Exadata systems for data warehouse At this point, Microsoft is an (SSIS) are still reported. Customers and OLTP are installed as of June inconsistent leader with lapses are still waiting for SQL Server 2011, purchased as quarter, half and in meeting market demands. 2012 to fix many of these issues, full rack units. Organizations considering Microsoft but for now, Microsoft’s execution Frequently, customers have multiple should have a clear understanding in the data warehouse market is units working on one warehouse. of the expected pace of product suffering. Internally, Oracle’s sales training enhancements in Microsoft’s road • The best summary of issues map. This is not a “bug” or release has focused on building value probably comes from one of issue, but a release management statements with customers and Microsoft’s customer references, issue. Make sure Microsoft’s prospects throughout the year – “Easy to use. Hard to make product features and release road the account positioning remains perfect.” Several customers map remains 10 months ahead of technology focused, but has begun report issues such as not scaling your plans to use new features and to move toward solution selling that well across a grid of servers, releases. Importantly, many aspects will become critical for promoting performance issues with complex of Microsoft’s vision align with the Oracle product use to support the queries, manual rebuilds of database LDW, but for now the vision suffers “logical data warehouse.” indexes, a need for multi-server from cross-product coordination, • Oracle introduced significant staging environments and more. which will become necessary as optimization in Oracle Database 11g These same references also report coordination across information (version 11.2.0.3) with cluster-wide difficulty in changing versions. Put asset types grows more prevalent in parallel operations executing in simply, based on the strengths and the market. memory (the large memory across 15
- 16. all Real ApplicationClusters [RAC] Oracle references also report continuing improvement. Finally, the nodes in the cluster are treated as that the products and appliances Oracle licensing model continues a single, large memory pool and appear to advance with additional to be an issue for these reference data is transformed/optimized for functionality at the desired pace customers who complain of different storage in memory, rather for the expanding requirements difficulty in cost planning. than on disk). Exadata has Exadata (functionality, data volume scaling • Oracle faces several challenges Smart Scan (to offload some and the types and number of with its LDW vision, but it has DBMS functionality to the storage anticipated queries). In 2011, time to respond and it is possible server), Exadata Hybrid Columnar references showed that some of the that an appliance can also contain Compression (which reduces previously reported issues regarding a complete array of services storage requirements and increases management tools were resolved. management, application servers performance) and Exadata Smart The result is that Oracle customers and data management platform to Flash Cache (up to about 5 TB have a pre-disposition to using manage a logical data warehouse; of flash memory to optimize data more Oracle products and this or that many appliances can be access and queries). includes data warehousing, which configured to address the LDW. According to Gartner’s clients and increases market sales generally and Oracle’s current strategy for client references (interviews began even more specifically, creates a analytics information management in late 2010, when Gartner spoke solid pipeline with existing Oracle remains tightly bound to a with over 100 Oracle Exadata customers. repository-based implementation warehouse implementations in the approach. Oracle Big Data past 18 months), Oracle Exadata Cautions Appliance includes its own NoSQL exhibits up to a tenfold increase in • Oracle clients are practical and database (based on BerkeleyDB) average performance, compared realistic in that they recognize the and an Apache/Hadoop distribution. with a similar workload running consistent strengths of products Prior to the publication of this Oracle on stand-alone hardware. and that any issues are inconvenient Magic Quadrant analysis, Oracle It should be noted this is increased or nuisances. Oracle has the announced that Oracle Big Data performance measuring Oracle most customer references of any Appliance incorporates the against Oracle. Oracle Exadata data warehouse platform within Cloudera version of Hadoop. is not required to deploy a data Gartner’s client base and so has Oracle already has the capability warehouse on Oracle Database, the greatest mix of comments in its various tools (such as Oracle but many of the solution design across these references. However, Fusion Middleware, Oracle Data issues solved by Oracle Exadata three types of issues are commonly Integrator and various DBMS must then be addressed by clients’ reported directly by customers. solutions) to address distributed implementation teams on their First, frequent “bugs” or software data and distributed processing, own. issues are reported in new releases, but its primary message for • Oracle’s customer references followed by a difficult patching warehousing still emphasizes the indicate the product is highly process. Second, when customers repository management approach in stable, consistently meets the experience specific performance favor of virtualization or distributed performance requirements of their issues involving complex queries, processes. This combination of use case needs, is easily supported they are difficult to resolve due challenges and events will not by a widely available skill base to “poor visibility into the actual necessarily force Oracle to isolate in the market and has a solid bottleneck.” on a single vision for the market, list of features (such as Exadata Some improvements in customer but the temptation to do so will be Hybrid Column Compression, management experiences are significant. During 2012 to 2013, Oracle Partitioning, Oracle RAC reported, but according to it will be important for Oracle to and Oracle Automated Storage references, these issues result establish its future vision for this Management). The net result is in somewhat higher efforts in market. that implementers who choose to administration and management of • As a new hardware vendor, proceed with Oracle will be able the environment. Gartner considers Oracle may pursue the vision of to proceed in a straightforward Oracle’s support regarding these hardware exclusivity or it may manner. issues to be effective and 2011 saw continue in its agnostic view of 16
- 17. hardware configurations. Gartner new senior management team in minimal tuning and administration as believes that an “appliance only” place and completed the process ParAccel’s strengths. view of the market is a dangerous in 2011. New marketing, a new • From its original inception, the strategy for Oracle to adopt. The chief executive and a new COO ParAccel Analytic Database (PADB) traditional data warehouse exhibits have already begun to change the was designed for multi-level, significant hardware and storage messaging, but more importantly, highly recursive analytics on self- optimization issues and solving them substantial changes have taken place joined datasets. Initially, customers represents a significant revenue regarding the company’s approach adopted this product for queries opportunity. The company’s to delivery. related to topics such as market announcement to support Hadoop/ As a result of the new team’s basket analysis and clinical trials MapReduce implementations via efforts, Amazon has invested data. However, this particular the introduction Oracle Big Data in ParAccel and other channel strength will also be highly adept in Appliance reinforces its approach partner agreements are in place providing social analytics support, to pursue large portions of the (or under final negotiation) with as the same processing techniques information management space in MicroStrategy, Cisco, Dell, Birst applying to market basket and trials an almost appliance-centric manner. and Accenture. ParAccel has also data analysis also provide significant Customers and implementers should secured a significantly expanded capability for social analytics and be wary of assuming that purpose- sales staff with experience of other many of the new big data dataset built appliances will solve their data warehouse DBMS vendors. issues. ParAccel customers will be analytics issues. This solution may Finally, the company has introduced able to utilize the new Hadoop not be the best implementation a licensing model with significant connectors to either bring data into approach for specific organizations, potential to serve as a cloud-friendly PADB for in-database operations whereas for other organizations model that supports the LDW. A (using MapReduce almost in it will be a preferred approach. single license is charged for based extraction, transformation and Additionally, Oracle has retained on the intended use, regardless of loading role) or to call to Hadoop a significant portion of Sun’s how much or what type of servers clusters and receive the results. personnel which also constitutes and storage are used. ParAccel has shown endurance as the Sun institutional memory and • ParAccel shipped version 3.1 in June its independent niche shrank, after 2012 will be an important year 2011. The new version included acquisitions of its analytic DBMS in determining how well Oracle high performance connectors with competitors. In 2011 it pursued executes on its hardware support Hadoop/MR and addressed an issue additional funding and changed its strategy. The company considers raised by some customers regarding executive team (CEO and heads of this an opportunity for innovative online and incremental backup and sales, engineering and marketing) resolutions to how it addresses restore. The version release also and hired a new set of senior sales market. included a query optimizer and directors to compete in a changed storage enhancement features. market, leveraging a reference base ParAccel The net result is that ParAccel is of enthusiastically loyal customers ParAccel (www.paraccel.com) offers an expanding its vision for the logical valuing its performance. A point analytic platform, based on a column- data warehouse (with much more release and aggressive partnering vectored database designed to enhance work to complete, specifically for strategy have also helped the multi-recursive analytics, especially virtualization support and semantic company to hold its ground and those exhibiting self-join requirements. management layers), but will need lay the foundation for future ParAccel has approximately 40 customers to address this larger environment accelerated growth. worldwide. either directly with new features or more likely through technology Cautions Strengths partnering. • ParAccel remains a small company. • ParAccel has been in operation for As a smaller vendor, ParAccel may There are clear advantages and five years with some of its earlier have an advantage in developing disadvantages from this in today’s customers exhibiting production new partner channels as they offer market. As a smaller company, DBMS operations for warehousing an alternative to the mega-vendor ParAccel will become increasingly for more than three years. In solutions. Customer references attractive to larger IT vendor 2010, the company began to put a show performance, support, partners as an alternative to 17
- 18. inviting a “stack”vendor into their • ParAccel has not won significant targeted the rising role of the data accounts. Additionally, vendors of mind share in the marketplace. scientist, as it promotes in-database new cloud offerings initially sought Therefore, for all of its new and external analytics against big to find low license costs but now partnerships, there are many data as well as planned support spend funds on their development competing solutions also vying for for hybrid analytics use-cases, models. attention with these new channel which combine structured data However, cloud offerings are partners and ParAccel cannot be and content (using a unified query maturing and this means that the all things to all partners. In the past language for SQL, like access to a functionality requirements for high year, the company has significantly wide variety of information asset availability and backup and restore revised its go-to-market plans based types in a single query structure). will become more important to on what PADB already does and From an execution standpoint, cloud solution providers – and introducing new channel strategies SAND Technology has completely is one of the unresolved issues for exposing the solution. It will revised its product distribution raised by PADB users. However, become increasingly important channels and sales organization, at the same time, ParAccel for ParAccel to focus on one or removing its heavy dependency as in increasing its research and two specific differentiators and SAP’s searchable archive solution development spending in this area, proceed accordingly, for example, by building up new revenue which represents a commitment seeking investment in the selected streams. Importantly, this answers to addressing cloud provider area, spending research and one challenge Gartner identified requirements. development funds in these areas, previously. identifying partners with use cases • ParAccel customer references In 2011, SAND Technology made and customers supporting these show that they are looking for significant progress relative to differentiators. Cloud analytics is features such as online scaling/ Gartner’s Magic Quadrant criteria one such area and ParAccel has re-organization and Lightweight and while remaining a niche vendor, made a good start. Social analytics Directory Access Protocol it has advanced in a year when the could be another. integration. Maturity issues are also entire market was challenged with raised, some of which could relate greater demands in both execution to product and others to a lack SAND Technology and vision and where most vendors of implementation experiences. SAND Technology (www.sand.com) is a actually moved backward or Customers also report DBMS tokenized, column-store DBMS vendor. It struggled to maintain their position. crashes and would like to see a has been in existence for approximately The company has also focused on storage environment that integrates nine years and reports more than 600 analysis of customer data, but not solid state storage with hard-disk customers using its tools via OEMs and only structured data, as stated drives along with better backup. direct sales (approximately 100 direct previously. SAND Technology’s new While customers suggest a solid- customers). SAND uses techniques such vision of the market following its state drive (SSD)/disk hybrid as tokenization and compression to improvements during 2011, could solution, Gartner does not strengthen its column-store design. Its mean that 2012 is a proof point necessarily support this approach. technology is used as an analytic engine year. ParAccel is monitoring specific and as an archive engine. • SAND Technology already had text use-cases for the benefits of search capabilities (sound/spell like, such a configuration. We hear Strengths relevance ranking and other text- mixed reviews on the standard • In 2011, SAND Technology based capabilities) and an extremely functionality, with infrequent endeavored to alter the overall compact column-store DBMS, as reports complaining of too many position in the data management well as cloud support functionality system parameters, slow real-time market and largely succeeded. Its (shared processor/storage and loads and even infrequent issues new vision is to promote the use of distributed processing management). with bulk loads. Microsoft’s TSQL software features and functionality In 2010, the company added is supported, but PADB doesn’t to support skilled data management managed, dependent, disconnected support extensions. In general, professionals so they can data marts, enabling synchronization customer references indicate more concentrate on those tasks which and updates to intermittently elasticity is required. continue to demand human insight. connected data marts. SAND Technology has specifically 18
- 19. As an archivetool, SAND is a publicly held company with • Customer references report Technology’s solution achieves regular earnings announcements). intermittent difficulties of a widely greater compression than other SAND reports that it has adequate varied nature. While SAND DBMSs because of its use of funding for its current run- Technology staff are deeply tokenization in addition to the rate and planning horizon. As a skilled in their products and data column-store and the resulting company experienced in partnering warehousing, skills are almost archive is SQL-accessible. SAND and channel delivery, SAND completely absent from the general Technology plans to introduce Technology can take advantage marketplace. Some customers Hadoop/MR capabilities and of the current trend for server, report that retiring or archiving extend its unified query language storage and network vendors that data off of SAND Technology is to put “Big data,” structured data, have determined it is in their best difficult (which is ironic as that text analytics/search and content interests to also become data was the company’s forte with SAP/ analytics into its UQL. The result is management software vendors. BW). Some customers report a lack that SAND Technology is another SAND’s internal expertise is also of metadata capabilities and poor vendor aggressively seeking to a valuable asset, both for building capability to manage data models identify its role in the LDW. channels or as an acquisition target. in the database. The high praise for • Almost all customer references The company has determined that SAND Technology staff and the report that the compression rate big data customer demands provide wide variety, but lack of specific of SAND’s column-store DBMS is it with an opportunity to win mind- trends for the issues reported, impressive. Additionally, those using share as a niche expert in this new seem to be a symptom of “there it as an archive or an enhancement demand area and is expanding its just aren’t enough experienced to SAP’s Business Warehouse consulting capability specifically for personnel” available. It is simply Accelerator report solid integration, this purpose. that SAND Technology’s support although direct interfacing proves and small professional services team • SAND Technology is present in more difficult when it is the primary cannot be everywhere at once. practically all the same large brand warehouse. SAND Technology name customers that every other refers to its core engineering as start up or mega-vendor lists as Sybase, an SAP Company “infinite optimization” because of their customer base. Additionally, In 2010, Sybase (www.sybase.com) was the tokenization and column store. they list many of the same channel acquired by SAP. Both have several DBMS References report significant partners. This is evidence more of products, but our analysis is based on advantage from indexing features, the fickle and inconsistent nature Sybase IQ, which was the first column- the power of the database engine, of enterprises regarding how they store DBMS and is SAP/Sybase’s primary ease of implementation and seek out new technology and data warehouse DBMS. It is available upgrades and support services how new solutions relate to their as both a stand-alone DBMS and a data (reporting that SAND Technology’s enterprise “standards,” which is to warehouse appliance, through several professional staff have high levels say that experimental projects or system integration vendors. We mention of data warehouse and product “skunkworks” and new technology SAP HANA here, but we do not have any expertise). It is also a good choice projects both ignore standards production references at the time of this for analytic data marts to support and do not necessarily see wide analysis. Sybase has thousands of Sybase the off-loading of workloads from adoption. IQ customers worldwide. an enterprise data warehouse. However, in SAND Technology’s With solid references and reported case, it has OEM and channel Strengths stability, more implementers during experience that has not yet resulted • With the release of Sybase IQ 15.3 2011 expressed an interest in in significant revenue growth. The and 15.4, Sybase IQ has moved SAND Technology’s vision. use of channels is imperative to the from being an analytics data mart to company’s success and development a data warehouse now supporting Cautions of two or three significant channel big data and the LDW. It has • SAND Technology is small. relationships is necessary at this added substantial mixed workload Despite the larger customer point. After a year of retrenchment, management, faster loading count attributed to it this year (by it is time to succeed and the capabilities (to address the biggest including its much smaller OEM indicators to watch are channel issue with disk-based column-store indirect customers), company uptake and delivery on its big data DBMSs), query parallelism across revenue is low (SAND Technology road map. multiple processors, the ability to 19
- 20. scale horizontally acrossa cluster Further, SAP positions Sybase IQ a standard technology in data of servers with MPP capabilities. as the DBMS of choice for the non- warehousing, as all vendors are Additionally, Sybase has added SAP data warehouse sitting beside adding this capability. Not only features to IQ such as integrated the SAP NetWeaver Business are there many other column- text search and analysis, in-database Warehouse for those customers store DBMSs available (such as data mining and Web-enabled requiring a separate DBMS for the Exasol, Infobright, ParAccel and language drivers such as Python, general data warehouse. To date, Vertica), but the general DBMS PHP and PERL – each targeted Gartner has not had a significant vendors are also adding column at a new generation of analytical number of inquiry clients reporting storage capabilities (such as EMC/ applications. that they use Sybase IQ combined Greenplum, IBM, Microsoft, Oracle With Sybase IQ 15.4, it has added with SAP NetWeaver Business and Teradata/Aster Data). Sybase capabilities to address the LDW, Warehouse in a dual warehouse IQ is the most mature and touts the such as distributed data sources approach. Most report they use SAP largest market share among column- and the ability to combine both NetWeaver Business Warehouse store DBMSs. structured and unstructured data in and another product (Oracle, IBM- However, Sybase IQ must now the same query. In addition, Sybase DB2, Teradata, for example) or differentiate beyond column-store IQ now has in-DBMS MapReduce Sybase IQ and another product. capabilities as it faces competition capabilities and connectors to We believe this will only increase as from most of the other vendors. Hadoop, adding to the ability to customers begin to use Sybase ASE Some of this pressure will be offset work with “Big data.” Finally, Sybase under the SAP Business Suite and as Sybase IQ matures to a complete IQ is the underlying technology of SAP BW. data warehouse solution and by Sybase RAP, The Trading Edition, • From our reference checks, Sybase SAP’s acquisition of Sybase giving including a built-in package for IQ enjoys some of the most loyal SAP/Sybase a complete DBMS time-series analytics. Sybase IQ and and happy customers. There were offering with Sybase ASE, Sybase IQ, Sybase RAP, in combination with virtually no negative comments. Sybase ESP (CEP) and SAP HANA. the Sybase Event Stream Processor The most common and consistent • A limitation with Sybase IQ is (ESP) complex event processing comment is the performance of the lack of a Sybase IQ appliance. (CEP) technology, creating a general IQ – all stating that the query Sybase has tinkered in this space real-time analytics platform for processing is extremely fast with no with agreements for appliances with applications requiring low-latency. optimization necessary. Customer third-party ISVs, but it has seen • Increasingly, Sybase IQ is on the references also claim that the little traction with this model. As shortlist for a complete data compression in Sybase can reach up appliances are widely accepted as warehouse solution, as described to 10 times, which is a high amount the model for a data warehouse, by our client inquiries. In addition, of compression for a column-store this may slow adoption of Sybase we see Sybase IQ increasing its DBMS. It is a combination of the IQ. Sybase does offer certified participation in and winning proof of compression and the column-store configurations and automated tools concepts (POCs), with performance that allows Sybase IQ to achieve for reference configurations, but up to 100 times that of its nearest the high performance for data the market is keenly aware of the competitor. Sybase has enhanced warehousing. It is important to difference forcing Sybase IQ out of the workload management note that Sybase exhibited both the final list of choices for a data capabilities of IQ over the past execution and vision similar to that warehouse in many situations. With several releases and now shows of 2010. The broader expectations SAP moving toward an appliance good performance in a complex represented in the 2011 to 2012 model with SAP HANA, we mixed-workload environment. The market influenced Sybase’s position believe we are not far from seeing market perception of Sybase has in 2012. a renewed effort with hardware changed over the past 18 months vendors to supply a Sybase IQ data since the acquisition by SAP. We Cautions warehouse appliance. seldom get inquiries asking about • As we described in our analysis • A new challenge for Sybase the viability of Sybase in the market. for 2011, column-store DBMS IQ is emerging because of the This has also strengthened the technology is gaining in customer acquisition by SAP. As SAP defines ability of Sybase to lead with IQ as acceptance and has become almost its go to market strategy for data a data warehouse. warehousing and information 20
- 21. management, SAP andSybase Teradata has continued to grow the • Teradata’s installed base continues have been slow to articulate to capabilities of its DBMS technology to grow. References are most customers where and how HANA by adding bi-temporal, columnar satisfied by the technology and Sybase IQ coexist in the IT support, automatic advanced scalability, stability, predictability environment, and its overall data compression options, for example, of performance, mixed workload management strategy regarding the further demonstrating its technology capabilities. For customers starting combined database offerings. investment. The attention to “Big new data warehousing projects, Gartner’s opinion is SAP would data,” the extensibility framework Teradata’s industry models continue like to see SAP HANA eventually for processing languages and mixed to be seen of great value. At the emerge as a data warehouse workload management, indicate same time, Teradata also offers appliance option and Sybase ASE a growing capability to support professional services with a cohesive would serve as the DBMS engine the LDW and Teradata’s strong strategy for warehouse deployment for the Business Suite. With this professional services provide which tools and technology with scenario, there is a need for clarity implementation field experience to expertise. References also report around the role of Sybase IQ, which support its deployment. near 100% uptime. has had significant capabilities added • Teradata’s management software, As a result, Teradata has the ability in recent releases, adding the ability including Teradata Active System to deliver a DBMS, an appliance, to virtually manage storage and Management (TASM) and Viewpoint, professional services, logical data even processing capacity. HANA is a clear strength. The management models and tools to manage has similar technology capabilities, software manages the entire data both the project delivery and the but these advances in Sybase IQ warehouse environment. As a eventual deployment. In 2011, may make it more suitable as an result, organizations are presented the addition and leveraging of the EDW platform. with multiple options as their data company’s purchase of Aster Data volumes and query complexity added a distributed processing grow by allowing management in extensible framework with the Teradata dual warehouses, single platforms, immediate addition of MapReduce Teradata (www.teradata.com) has a various appliances and more. capabilities (product integration is 30-year history in the data warehouse Teradata’s Unity supports multi- under way to its overall portfolio), market supplying a combination of tuned system deployments and confers the with the potential for further hardware and analytic specific database ability to gain a single operational extensibility (for example, graph software. Teradata has more than 1,000 view across Teradata systems language commands). The addition end-user organizations as customers and to move and manage data of new data types (such as bi- worldwide. and applications between multiple temporality) has extended the analytical systems in an enterprise. analytic capabilities. At the same Strengths Teradata has a formalized strategy time, the 2600 series of Teradata’s • Teradata’s products include appliances is seeing increasing for combining older equipment departmental, data mining- market adoption and providing with new generations (“investment focused and enterprise solutions. entry level customers with a lower- protection”) the use of virtual Its portfolio also includes cloud priced solution. work units can be distributed, and big data solutions. Aster with more work units on newer Data added new capabilities to generation nodes, relieving some Cautions Teradata’s product line (such as of the performance pressure on • The pre-eminence of appliance MapReduce, unstructured data and older equipment. In addition to an solutions in data warehouse bids graph analysis). The acquisition enterprise active data warehouse results in a more competitive demonstrated a persistent strategy for operational analytics support, environment where Teradata needs to address the growing role of the features such as object access and to educate on its differentiating data warehouse as an information query resource filtering, throttles value. This has increased Teradata’s management platform which goes that can be applied to named users, exposure to competition. In beyond addressing data-volume- connections or the entire system the smaller data warehouse based issues, but also information and performance groups based markets, Gartner continues to variety, velocity issues and on relative priority contribute to note that some clients report complexity of information types and software management capabilities. that they selected competitors analytic processing. 21
- 22. because there was“no discernible • Gartner clients indicate during shared-nothing deployment on MPP difference” in performance between inquiries that they do not use the clusters of commodity servers with Teradata’s offerings and those of platform to its full potential because built-in high availability, including its competitors’ appliances in such some of its optimization features active replicas and automated situations. are difficult to understand and node recovery, Vertica now claims The same customers also report use. However, the most important to have scaled to 460 nodes. that their warehouse workloads issue is that prospective clients Vertica was an early leader in data are either somewhat predictable, are expected to understand the compression, the use of memory or that they usually require four differentiation between Teradata’s and disk storage in combination of the six data warehouse DBMS appliance offerings and the for three levels of storage for workloads that Gartner defines enterprise-class product when hot and cold data, connectivity to and rarely five. In such cases, deciding on a purchase. Most entry- Cloudera’s Distribution for Apache Teradata must leverage its 2600 level and even second-generation Hadoop (CDH) and extensibility via series products, which have lower warehouse implementers have an software development kit that first cost or intentionally lengthen difficulty determining the future has permitted adding in-database the sales cycle to allow for an needs of their users. execution for pattern matching, education process regarding the In short, prospective customers statistical and linear regression, enterprise-class benefits of higher need to be educated about geospatial, social graphing and more. end solutions. Teradata’s approach before they can Its separation of a write-optimized • IT vendor management and determine the difference between store in memory from a read- purchasing practices, as well as its products and more importantly, optimized store on disk, which current market consolidation have between Teradata’s appliance and facilitated improved load speed, renewed the appeal of a single- those offered by competitors. In the not only tackled a substantial vendor approach to IT shops. current survey, clients have outlined problem for columnar databases, Teradata continues to take a best- the overall cost of the Teradata it also pointed the way to more of-breed approach and is aware of platform as a negative point. general use of in-memory database this issue, as its partnerships involve However, while the price point technology. Release 5, which arrived both marketing and technological seems to be an issue, this has not a month after Vertica’s acquisition cooperation. We advise that come up as a reason to consider closed, added rich event and organizations should focus on an alternative technology for the statistical processing, in-database decision criteria relating to mixed overall data warehouse platform user-defined analytic functions, workload demands, balanced system and has generated more interest in geospatial processing and bulk management and data optimization managing older and less frequently transfer between clusters. and a timeline for adding big data accessed data with cheaper options. • Vertica’s customers cite its pricing issues, which are critical factors model (based on the amount of in selecting the data warehouse Vertica SSED loaded into the DBMS, rather DBMS. Vertica, a HP company (www.vertica. than on the number of users, However, some organizations com) offers a fully integrated column- servers, chips or cores) and its will adopt these solutions more store analytic DBMS with a number performance as key reasons for slowly and the result could be that of additional capabilities for high selection and satisfaction. Gartner Teradata commits resources for performance and high availability. Derived spoke with several of Vertica’s prospect education with a lower from research originally done at the claimed 500 customers using more rate of conversion to revenue, Massachusetts Institute of Technology, than 100 TB of data (a number compared to the majority of the its acquisition by HP was closed in May of which have a year or more of data warehouse market. However, 2011. Vertica has more than 500 direct production history) that also cited Teradata has exhibited lengthy and OEM customers. HP also offers an availability and reliability, as well market experience accomplishing appliance version. as ease of version migration. Set a balance in the market between up and automated database design championing new practices and Strengths were also frequently mentioned, supporting product development together with price have helped the • Vertica has earned its reputation as to support new analytics and data company reach into the midmarket an advanced DBMS with a rich array warehouse practices. for prospects. of features. Designed for scalable 22
- 23. • Flexible deploymentoptions • HP’s extensive partnerships seem Vendors Added or Dropped have helped Vertica’s reach to be an asset, but could also create Exclusion from the Magic Quadrant since its earliest days and it was challenges. For example, in the data should not disqualify a vendor or its the first DBMS to run on cloud warehouse appliance segment of products from consideration. Clients infrastructure, using Amazon the market, Microsoft is a strategic should examine vendors and products Elastic Compute Cloud (EC2). Its global partner whose flagship SQL based on their specific needs and addition of bulk transfer features Server product will receive its circumstances. for rapid provisioning and cloning first refresh in several years during of clusters across public and private 2012 and HP is one of two vendors We review and adjust our inclusion cloud and dedicated hardware Microsoft will depend on to sell its criteria for Magic Quadrants and continues this theme. This will Parallel Data Warehouse. MarketScopes as markets change. As continue to allow Vertica to How HP’s sales force will treat a result of these adjustments, the mix perform more POCs and should these two different offerings will of vendors in any Magic Quadrant or be an opportunity to leverage HP’s create uncertainty, as marketing MarketScope may change over time. A substantial commitment to cloud of the Vertica appliance and its vendor appearing in a Magic Quadrant or infrastructure. Microsoft appliance have been MarketScope one year and not the next Vertica will also continue to benefit underwhelming as Oracle’s Exadata does not necessarily indicate that we have from its SaaS and embedded models has dominated the airwaves. HP’s changed our opinion of that vendor. and presumably will leverage the acquisition of Autonomy in October This may be a reflection of a change in the ecosystem of HP partners to 2011 adds another element that market and, therefore, changed evaluation good advantage. Finally, Vertica could prove to be synergistic or criteria, or a change of focus by a vendor. Community Edition (free up to 1 delay the formation of a coherent TB, 3 nodes) was introduced along strategy while the management Added with version 5.0 to exploit HP’s turnover settles. A substantial drop In 2012, we have added the vendor reach to fill its pipeline. Follow up is in inquiries to Gartner reinforces Exasol, from Nuremberg, Germany to also a key opportunity. the impression of a troubling the Magic Quadrant. Exasol is a small slowdown of Vertica’s momentum. DBMS vendor in Nuremberg, Germany Cautions • The column-store DBMS is no and has been in business since 2000 with longer sufficient for differentiation. the first in-memory column-store DBMS, • HP’s acquisition brings uncertainty Vertica continues to face named EXASolution, available since 2004 along with opportunity. It claims competition from more mature and primarily used in deploying end- substantial retention of Vertica DBMS vendors as they add user specific or departmental analytic staff, but its recent history of column-store compression and applications. unsuccessful technology acquisitions, public dithering over strategy and other capabilities (hybrid column Dropped management turmoil do not inspire and row store) to their DBMSs. We were unable to collect sufficient confidence. HP’s prior acquisitions Oracle Exadata’s and Sybase’s information, including client references, of data warehousing services marketing have drowned out market information, product road leader Knightsbridge and system Vertica’s message, aided by HP’s map, and strategic direction, to qualify integrator EDS did not result in relative absence from the scene. Illuminate for inclusion in this Magic significant marketplace presence or Vertica must continue to add high Quadrant. visible wins in the data warehouse data volume customers with large implementation services market and numbers of users to prove its Aster Data Systems was acquired by much of the experienced talent has improving workload management Teradata. departed, which leaves EDS lacking capabilities or be relegated to any significant data warehouse analytic data mart installations Vertica Systems was acquired by HP. skilled teams and HP is weak in instead of bidding for the strategic data warehouse implementation information platform role HP Inclusion and Exclusion Criteria knowledge and experience. None aspires to build for it. • Vendors in this market must have of the customer referees we spoke DBMS software that has been to mentioned HP (negatively or generally available for at least a year. positively). We use the most recent release of the software for our evaluation. We do not consider beta releases. 23
- 24. • Vendors musthave generated Table 1. Ability to Execute Evaluation Criteria revenue from a minimum of 10 Evaluation Criteria Weighting verifiable and distinct organizations with data warehouse DBMSs in Product/Service high production. Overall Viability (Business Unit, Financial, low • Customers in production must Strategy, Organization) have deployed enterprise-scale data warehouses that integrate Sales Execution/Pricing low data from at least two operational Market Responsiveness and Track Record high source systems for more than Marketing Execution standard one end-user community (such as separate business lines or differing Customer Experience high levels of analytics). Operations low • Support for these data warehouse DBMS products must be available Source: Gartner (February 2012) from the vendor. We also consider open-source DBMS products from vendors that control or participate Specific Criteria nature of configurations and workload in the engineering of DBMSs. Product/service includes the technical mix), general customer perceptions of the attributes of the DBMS. We include high vendor and its products, and the diversity • Vendors participating in the data availability/disaster recovery, support and of delivery models. We also consider warehouse DBMS market must management of mixed workloads, speed the vendor’s ability to adapt to market demonstrate their ability to deliver and scalability of data loading, and support changes and its history of flexibility when the necessary infrastructure and for new hardware and memory models. it comes to market dynamics, including services to support an enterprise These attributes are measured across a use of POCs as required by the market. data warehouse. variety of database sizes and workloads. • Products that include unique file We also consider the automated Marketing execution explores how management systems embedded in management and resources necessary to well the vendor understands and builds front-end tools, or that exclusively manage the data warehouse, especially as its products in response to the needs of support an integrated front-end it scales to accommodate larger and more customers (from novices to advanced tool, do not qualify for this Magic complex workloads. implementers), and how it develops Quadrant. offerings to meet those needs and the Overall viability includes corporate needs of the market in general. We also Evaluation Criteria aspects such as the skills of the consider the vendor’s geographical ability personnel, financial stability, research to deliver solutions. Ability to Execute and development investment and merger Ability to Execute is primarily concerned and acquisition activity. It also covers We evaluate customer support and with the ability and maturity of the the management’s ability to respond to professional services as part of the product and the vendor organization. market changes and the company’s ability customer experience criterion, together Criteria under this heading also consider to survive market difficulties (crucial for with input from customer references. the product’s portability, its ability to long-term survival). Also considered are the track record run and scale in different operating of POCs, customers’ perceptions of the environments (giving the customer a Under sales execution/pricing we product, and customers’ loyalty to the range of options), and the differentiation examine the price/performance and vendor (this reflects their tolerance of its between data warehouse DBMS solutions pricing models of the DBMS, and the practices and can indicate their degree of and data warehouse appliances. Ability to ability of the sales force to manage satisfaction). Execute criteria are critical to customers’ accounts (judging from feedback from our satisfaction and success with a product, clients). We also consider DBMS software Operations covers the alignment of so customer references are weighted market share. the vendor’s operations, as well as heavily throughout. whether, and how, they enhance its Market responsiveness and track ability to deliver. We also include channel record covers references (for example, partnerships and the vendor’s ability to number and size of client companies, create and use a partnership model. 24
- 25. Completeness of Vision Table 2. Completeness of Vision Evaluation Criteria Completeness of Vision encompasses a vendor’s ability to understand the Evaluation Criteria Weighting functionality necessary to support Market Understanding high the data warehouse workload design, Marketing Strategy standard the product strategy to meet market requirements, and the ability to Sales Strategy standard comprehend overall market trends and Offering (Product) Strategy high to influence or lead the market when Business Model standard necessary. A visionary leadership role is necessary for the long-term viability of Vertical/Industry Strategy low product and company. A vendor’s vision Innovation high is enhanced by its willingness to extend Geographic Strategy low its influence throughout the market by working with independent, third-party application software vendors that deliver Source: Gartner (February 2012) data-warehouse-driven solutions (such as BI). A successful vendor will be able not only to understand the competitive landscape of data warehouses, but also to ability to communicate its vision to its DBMS, specifically for data warehouses. shape the future of this field. field organization and, therefore, to clients The use of new storage and hardware and prospective customers. models is key. Increasingly, users expect a Specific Criteria DBMS to become self-tuning, reducing the Offering (product) strategy covers the resources involved in optimizing the data Market understanding covers a areas of product portability and packaging. warehouse, especially as mixed workloads vendor’s ability to understand and shape Vendors should demonstrate a diverse increase. Also addressed here is the the data warehouse DBMS market and strategy that enables customers to choose maturation of alternative delivery methods show leadership in it. In addition to what they need to build a complete data such as infrastructure-as-a-service and examining a vendor’s core competencies warehouse solution. We also consider the cloud infrastructures. in this market, we consider its awareness availability of certified configurations and of new trends, such as the increasing appliances based on the vendor’s DBMS. We evaluate a vendor’s worldwide reach sophistication of end users, the growth in and geographic strategy by considering data volumes and the changing concept of Business model covers how a vendor’s its ability to use its own resources in the enterprise data warehouse. model of a target market combines with different regions, as well as those of Marketing strategy refers to a vendor’s its products and pricing, and whether subsidiaries and partners. This criterion marketing messages, product focus, and it can generate profits with this model, includes a vendor’s ability to support ability to choose appropriate target judging from its packaging and offerings. clients throughout the world, around the markets and third-party software vendor clock, in many languages. partnerships to enhance the marketability We do not believe that vertical/ of its products. For example, whether industry strategy is a major focus of the Quadrant Descriptions the vendor encourages and supports data warehouse DBMS market, but it does independent software vendors in its affect a vendor’s ability to understand its Leaders efforts to support the DBMS in native clients. Items such as vertical sales teams The Leaders quadrant contains the mode. and specific vertical data models are vendors that demonstrate the greatest considered here. support for data warehouses of all An important criterion is sales strategy. sizes, with large numbers of concurrent This encompasses all channels and Innovation is a major criterion when users and management of mixed data partnerships developed to assist with evaluating the vision of data warehouse warehousing workloads. These vendors sales and is especially important for DBMS vendors in developing new lead in data warehousing by consistently younger organizations, as it enables them functionality, allocating R&D spending and demonstrating customer satisfaction and greatly to increase their market presence leading the market in new directions. It strong support, as well as longevity in while maintaining a lower cost of sales. also includes a vendor’s ability to innovate the data warehouse DBMS market, with This criterion also includes the vendor’s and develop new functionality in the strong hardware alliances. Hence, Leaders 25
- 26. also represent thelowest risk for data To qualify as Visionaries, vendors must to compete against each other. In fact, at warehouse implementations, in relation demonstrate that they have customers in times a niche vendor will compete and to, among other things, performance production, in order to prove the value win due to the use case and customer as mixed workloads, database sizes and of their functionality and/or architecture. demands against a leader. This is a complexity increase. Additionally, the Our requirements for production function of new and different demands market’s maturity demands that Leaders customers and general availability for in the market from end-users who are maintain a strong vision for the key at least a year mean that Visionaries implementing with and using products. trends of the past year: mixed-workload must be more than just startups with a The data warehouse market is redefining management for end-user service-level good idea. Frequently, Visionaries will itself and we fully expect the vendors to satisfaction and data volume management. drive other vendors and products in adjust to these new demands. this market toward new concepts and Challengers engineering enhancements. In 2010, the There is more on all of these trends The Challengers quadrant includes Visionaries quadrant was thinly populated in the Market Overview section of stable vendors with strong, established with vendors meeting demand from this document. It is certain that the offerings but a relative lack of vision. some market segments for aggressive information management market and These vendors have presence in the data strategies for specific functions, such as thus the data warehouse DBMS market, warehouse DBMS space, proven products the use of MapReduce for large-scale data is undergoing significant changes and and demonstrable corporate stability. analytics and massive process scaling in the result will be the refinement of this They generally have a highly capable heterogeneous hardware environments. Magic Quadrant’s evaluation criteria in execution model. Ease of implementation, coming years reflecting this divergence of clarity of message and engagement with Niche Players tradition from the last 20 years toward clients contribute to these vendors’ Niche Players generally deliver a highly the future market demands. It is highly success. Challengers offer a wide variety specialized product with limited market probable that traditional execution of data warehousing implementations appeal. Frequently, a Niche Player will remain important while market for different sizes of data warehouse provides an exceptional data warehouse understanding of traditional practices with mixed workloads. Organizations DBMS product, but is isolated or limited becomes less important for visionaries often purchase Challengers’ products to a specific end-user community, region and leaders. initially for limited deployments, such or industry. Although the solution itself as a departmental warehouse or a large may not have limitations, adoption is Over the past 20 years, the data data mart, with the intention of later limited. warehouse market has exhibited a scaling them up to an enterprise-class This quadrant contains vendors in several pattern wherein visionary architectures deployment. categories: are adopted by less than 20% of organizations as much as five to seven • Those with data warehouse DBMS years before they become more widely Visionaries products that lack a strong or a accepted. In 2011, the warehouse began Visionaries take a forward-thinking large customer base. a visionary metamorphosis away from a approach to managing the hardware, • Those with a data warehouse DBMS repository-only based solutions toward a software and end-user aspects of a data that lacks the functionality of those coordinated information processing and warehouse. However, they often suffer of the Leaders. delivery semantic. Gartner calls this new from a lack of a global, and even strong • Those with new data warehouse archetype the “Logical Data Warehouse” regional, presence. They normally have DBMS products that lack general (LDW) and it has a significant impact on smaller market shares than Leaders customer acceptance or the proven the vision expectations in the 2012 Magic and Challengers. New entrants with functionality to move beyond niche Quadrant. The LDW is only one potential exceptional technology may appear in status. Niche Players typically new set of best practices for warehousing this quadrant soon after their products offer smaller, specialized solutions and analytics data management. The become generally available. But, more that are used for specific data warehouse remains mission-critical and typically, vendors with unique or warehouse applications, depending even more so from 2012 forward (see exceptional technology appear in this on the client’s needs. Note 4). quadrant once their products have been generally available for several quarters. Others include wholesale replacement The Visionaries quadrant is often Context of the warehouse with search, content populated by new entrants with new The apparent backward movement of analytics and MapReduce clusters for architectures and functionalities that are nearly all the vendors does not specifically example, while dropping the centralized unproven in the market. indicate a change in their relative ability repository completely. Gartner strongly 26
- 27. asserts the positionthat the LDW is Acquisitions continued in 2011 with the data warehouse DBMS vendors must the correct vision for the new wave major vendors adding big data solutions participate in the marketplace to support of analytics best practices that is now and the smaller vendors finding new big data solutions and how well vendor emerging. However, current adoption is opportunities with new partners. In 2011, solutions executed throughout 2011 embryonic and anticipated uptake will be the role of Sybase as part of SAP became had significant impact on their execution incremental for the next two years. more obvious and EMC established a rating. It is very likely that traditional data clearer road map of how Greenplum warehouse solutions will become factors into its larger customer base. Most importantly, the solution can be any common practices within the next two form of execution, a separate product, years, eclipsed by this new approach. Aster Data (a significant visionary in 2010 integrated with current products or even The result will be a radical shift in how and 2011) was acquired by Teradata and via a partner (see “Data Warehousing this particular Magic Quadrant analysis is Vertica was added to the HP portfolio. Trends for the CIO, 2011-2012” for new presented by 2014. It is worthy to note At the same time, longtime megavendor and continuing trends in this research). that vendors such as EMC/Greenplum, rivals IBM and Oracle continued In 2011, we saw vendor consolidation HP/Vertica, ParAccel and SAP/Sybase to expand their offerings, with IBM with the larger vendors acquiring many of continue to apply pressure in the market focusing on improved solution selling the innovators and adding their visionary to move visionary concepts forward. for a complete data warehouse and capabilities to more traditional market The leading vendors are demonstrating analytics delivery and Oracle expanding delivery this had the effect of causing a differing levels of response to this its appliance, ready to run offerings to return to a linear trajectory in the analysis pressure and how they respond will maintain its strong ability to execute in results, which means that innovations in factor significantly into their ability to the market. delivery models, big data beyond volume maintain leadership in visionary delivery management and distributed analytics to the market. Throughout 2011, Microsoft re-focused could be a wide-open, visionary space in on its vision for the data warehouse 2012. The most significant execution change market and the market is responding with in the 2011 market was the rapid rise guarded optimism, so good execution This Magic Quadrant analysis deals with in the demand for “Big data,” which was in 2012 is essential. It is important the key capabilities of your business seen as visionary in 2010. During 2011, to remember that almost any data analytics framework and your information we also saw an increased demand for warehouse DBMS can deliver batch/ capabilities framework. As such, it should professional services and demands for bulk load support, basic reporting and interest anyone involved in defining, increased specific data warehouse skills essential OLAP. It is the addition of data purchasing, building and/or managing a in the customer base. The result is a mining, operational BI and real-time data warehouse environment – notably much larger playing field, meaning the components which create complexity. CIOs, chief technology officers, members overall represented area for this Magic As all data warehouse workload “mixes” of business intelligence (BI) competency Quadrant analysis is much larger in 2012 are different in each organization and centers, infrastructure, database and and will challenge almost every vendor in the adoption of warehouse capability data warehouse architects, DBAs and IT both vision and execution. Some vendors also varies, it is imperative to do an purchasing departments. responded more aggressively than their appropriate assessment for needs and market counterparts relative to the requirements to determine the best Gartner’s Magic Quadrant process also LDW for big data market demands and platform for any implementation. includes contacts and research regarding integrating their professional services vendors that ultimately are not included organization with product delivery. Many trends, such as poor data in this analysis. This is the process, which Appliance adoption is continuing. warehouse performance, heavy adds new vendors to the research on competition and widely disparate a periodic basis (for example, Exasol in The DBMS market continued its growth marketing claims, will continue through 2012). In 2013, we are planning for a in 2011, with niche vendors pushing 2012 and beyond. In 2011, we saw a section in the Magic Quadrant analysis on into challenger and visionary positions. surge in a desire to explore and pilot vendors considered but not included. (Gartner tracks DBMS market share and big data analytics and processing, in this it is possible to create approximations case, mistakenly isolated to MapReduce Market Overview of the data warehouse market size solutions in most inquiries. However this In 2011, the economic “new normal” based on informed analysis, but here surge in interest is also driving the use of became better understood and we are referring to DBMS and not data MapReduce technologies into Gartner’s organizations in nearly every vertical warehouse market share.) “Trough of Disillusionment.” As a result, market began to target more holistic 27
- 28. and comprehensive effortsto leverage be uneven and very few (if any) fully performance relative to their peers. the information available to them as a deployed LDWs will exist by the end of Organizations expect architecture and means of differentiating their business 2012. We anticipate that it will remain implementation leadership from vendors’ performance. a strong component of vision evaluation professional services and support criteria for some time. organizations and/or their partner and In 2010, revenue in the relational DBMS distribution channels. market was up almost 10% over 2009, A more subtle aspect of the LDW is at $20.7 billion. There are many factors that it completely changes the definition Gartner first identified this trend as part contributing to the DBMS market growth, of “size” of a data warehouse away of the overall data warehouse delivery in only one of which is the implementation from repository concepts to access its Magic Quadrant analysis in 2010 (for of data warehouses supporting and performance. Performance and 2009 market research), where we stated analytics. However, the combination of information asset value defined by ease of vendors have placed “significant and consumerized information management access and the ability to apply information appropriate emphasis on the formalization with consumer-driven analytics makes to use cases will become the new and of professional services to support data a strong case for asserting that data most important value-metrics. Even with warehouse delivery in 2009. Some have warehouse implementations were a early adoption, the impact on the Magic purchased consultancy organizations, significant contributor to market growth Quadrant this year is primarily related to others have introduced formal approaches in 2011. vendor vision. In “Does the 21st Century for identifying best practices from their “Big Data” Warehouse Mean the end existing field delivery teams and are DBMS licenses can be implemented of the Enterprise Data Warehouse?” creating standards of delivery based on for any information management use Gartner released the LDW concept those experiences.” In our 2011 Magic case (for example, analytics, OLTP, after nearly 20 months of tracking the Quadrant analysis, it became an important metadata management and master data phenomenon. evaluation execution criteria and under a management), which means that the size solution selling model throughout 2011, of the data warehouse market can be The volume, variety, velocity and organizations implementing warehouse estimated, rather than reporting actual complexity issues which constitute big attributed significant positive effects to the revenue. data quantitative capabilities and being presence of qualified professional services able to address them, constituted a teams. The LDW demand in the marketplace significant portion of the ability to is significant, but is being pursued execute in 2011 and we anticipate that Appliances remain popular and most data primarily by analytics architecture leader its importance will increase by the end of warehouse environments will eventually organizations. The LDW incorporates a 2012. In “’Big Data’ Is Only the Beginning include an appliance somewhere. combined infrastructure of repositories, of Extreme Information Management” However, the market has not yet data virtualization, distributed processing, Gartner defined a twelve dimensional determined the acceptable threshold system auditing metadata, end-user representation of big data solution design, regarding “how much” of it is appliance service level declarations and a decision which we call Extreme Information driven. It is important to note that while engine to determine which of the data Management issues. Prior to the complete appliances continued to be popular in solutions available meet the negotiation vision, Gartner expressed the quantitative 2011, the No. 1 complaint is inflexibility between the SLAs and the system aspects of extreme information regarding hardware. Further, the appliance auditing results best. management (big data). Gartner first market (even if all of Teradata, Exadata, identified this trend; stating “Many IBM/Netezza and others are included) During the period from September organizations that are creating large after 30 years of Teradata, seven years 2010 through November 2011, Gartner amounts of data that need to be analyzed of Netezza and three years of Oracle inquiries mentioning some aspect of the and used are turning to MapReduce- Exadata, constitutes less than 15% of the LDW design increased from virtually nil enabled DBMSs to gain performance by delivered units in the data warehouse to approximately 15% of data warehouse processing these large sets of data in a total market. Given that most large data inquiries. We anticipate that inquiries parallel environment”. warehouses witness a major revision and regarding the LDW hybrid environment retrofit between years five to seven of will increase at a faster rate, with some As part of the answer to a troubled their life cycle, the timing indicates that aspect of the approach appearing in 25% economic environment, the data 2012 to 2013 could see an acceleration of (or slightly higher) of data warehousing warehouse has become a central element appliance adoption. advice inquiries by the end of 2012. in information management and analytics However, actual market adoption will for organizations in differentiating their 28
- 29. In 2011, thedraw toward analytics has two identical “warehouses,” while others chosen vendor. Additionally, Gartner provided significant new opportunities for advise their customers to scale a single clients report that one of the most entrants into the market or simply new warehouse with more processing capacity important results of POCs is simply opportunities for some struggling vendors and memory as well as load balancing, assessing how quickly a solution can be already in the market. The noSQL with most of the leaders offer multiple deployed and configured for operations, movement (which is really not only SQL) alternatives. even though the vendor POC team can has opened the door to information overtly influence this experience. For this repositories that more closely resemble Gartner clients reported an increasing same reason, while lab-based POCs are content systems than relational databases. number of “dual” warehouses in 2011. acceptable to examine workload mix and Sometimes, these warehouses are two- performance metrics in general, they are The market also changed in another way. tiered with a base warehouse underneath not specific for giving information on your Many of the previous visionaries were and a query-optimized second warehouse actual time to delivery. acquired by the mega vendors (IBM/ in production above it (these are Netezza, HP/Vertica, SAP/Sybase and complete copies of the warehouse simply The data warehousing solution space now EMC/Greenplum) and hardware and stored differently). These are sometimes exhibits two highly distinct populations, infrastructure vendors found themselves referred to as side-by-side operations. traditional data warehouses and searching for less threatening hardware However, regardless of what it is named, hybrid-enabled warehouses combining partners to share and build their own this is an optimization strategy based on structured data and content (either in channels. As a result, opportunities separating physical workloads – usually one management system or via database for the smaller data warehouse DBMS isolating loads and basic reporting or basic management system-enabled functionality vendors abound as the market builds OLAP from the more data-intensive data such as UDFs, managed external a new set of options for configured mining efforts. processing and so on). The traditional infrastructures and eco-system partners. data warehouse solution continues to Organizations are also seeking alternatives pursue high performance, integrated At the same time, some challenges have to the traditional model where they data analysis, primarily for structured or emerged for the traditional leaders. own software licenses and servers and tabular data. The performance demands in Oracle has more than three years storage. The managed services warehouse this space continue to rise. experience in the market with Exadata, is gaining market traction and companies which is an inflection point for managing like IBM’s managed services, HP (via The hybrid warehouse takes many forms, and scaling most data warehouses, making EDS) and even Cognizant (a professional but in general, the market is demanding 2012 a bell-weather year for determining services vendor) offer one alternative. repository, virtualization and distributed if Oracle’s appliance strategy will continue Data warehouse database as a service processing capability, managed by a single to grow or pause. (dbaaS) providers offer a warehouse system and able to respond to various use on a platform from companies such as cases, which is another incarnation of the Additionally, IBM has begun to leverage Kognitio and 1010data. dbaaS vendors logical data warehouse. the Netezza acquisition by gaining such as Kognitio and 1010data offer significant new customers (especially DBMS implementations hosted on behalf In addition, we believe the data relative to Linux). Teradata’s appliance of its customers with the hosted database warehouse DBMS market will continue strategy for its 2600s and 1600s series off-site. to change in 2012 to fulfill the demand of products has resulted in both an “on for high speed, lower latency and large ramp” for more Teradata customers and We continue our stance in 2011 and volumes of data brought about by new a perimeter defense against the incursions 2012 that POCs are not only mandatory high-value applications. of other appliance vendors. to evaluate implementation options, but should be comprehensive examples As stated in the previous version of this The market demand is clear in that more of each of the workload types, which Magic Quadrant analysis, we believe data miners are competing with more regularly occur in your own data vendors have begun to establish their reporting and more basic analytics in a warehouse. positions in preparation for a major manner that is approaching a 24 hour/ battle over the data management role day operational window. Some vendors Organizations executing POCs using in the enterprise. Vendors that do not have embraced the dual strategy by their own data at their own sites have differentiate their offerings will either developing or acquiring fast replication reported experiences different from leave the market by choice or be forced and synchronization technology between common market experiences for their out by economic necessity. 29
- 30. Once vendors haveestablished their • The rising demand for combining • A DBMS has to support workloads positions, the major tussle will begin, structured and content information. ranging from simple to complex toward the end of 2013. It is becoming and to manage mixed workloads in clearer that this will represent a major Organizations have expressed an interest many different combinations. upheaval in the market, one that the in technical solutions that is starting to • Users are getting better at creating larger vendors need to prepare for and erode the 2009 effect where everyone specific SLAs and the implications of that will give smaller vendors a market sought vendor financial viability. A spirit not meeting them are more serious. opportunity. of experimentation, pilot schemes and prototypes has re-emerged. Organizations The data warehouse DBMS has evolved The new analytics infrastructure is a are reminded to closely align their from being an information store to a combination of services, platforms, analytics strategies and vendor road maps support for reporting and traditional repositories, metadata and optimization when choosing vendors. BI platforms and now into a broader techniques which all work in concert. analytics infrastructure supporting The “data warehouse” will become “data The data warehouse DBMS market is operational analytics, performance warehousing” – again. The concept of a complex, with a mix of mature and new management and other new applications single grand repository managing all the products. Its complexity reflects many and uses, such as operational BI, real- information for analytics use cases will be factors, such as: time fraud detection or consumer increasingly challenged and near 2017, a • The need for DBMS systems to experience personalization and new infrastructure of highly-distributed support database sizes ranging from operational technologies (technologies processes and information assets will have the 2 terabytes to 1+ petabytes. that stream data from devices such as emerged. • The complexity of data in data smart meters). Organizations are adding warehouses, not only in terms additional workloads with OLTP access As described in “The State of Data of interrelationships, but also of and data loading latency is falling to near- Warehousing in 2011” several aspects of desired data types. continuous loading. this battle are emerging: • The combination of repositories, • The fact that data warehouses are There are many other aspects to the data virtualization and data buses built on many different hardware data warehouse DBMS market, such is now possible, given the state of and operating systems, which a as pricing models, geographic reach, hardware technology. DBMS needs to support. partner channels, third-party software • The reduced influence of BI • The growing and regularly changing partnerships and data warehouse services platform optimization, in favor of variety of operations performed in (see “The State of Data Warehousing, DBMS optimization. data warehouses, which requires 2012” [forthcoming] and “Data continuous management of the Warehousing Trends for the CIO, 2011- • The increasing influence of master DBMS. 2012” for further information on these data management and data quality. trends). • The demand for cloud solutions. Source: Gartner Research, G00219281, Mark A. Beyer, Donald Feinberg, Merv Adrian, Roxane Edjlali, 6 February 2012 Note 1 Definition of a Data Warehouse Appliance A prepackaged or preconfigured, balanced set of hardware (servers, memory, storage and input/output channels), software (OS, DBMS and management software), service and support, sold as a unit with built-in redundancy for high availability and positioned as a platform for data warehousing. Further, it must be sold on the basis of the amount of SSED (“raw data”) to be stored in the data warehouse and not of configuration (for example, the number of servers or storage spindles). Our performance criteria have some flexibility to accommodate vendors that have several variations, based on desired performance SLAs and the type of workload intended for the appliance. Our primary concern is that the user (buyer) cannot change the configuration due to budgetary issues, thereby adversely affecting the performance of the appliance. 30
- 31. Note 2 Definition ofMixed Workload The modern complex mixed workload consists of: • Continuous (near-real-time) data loading, similar to an OLTP workload (due to the updating of indexes and other optimization structures in the data warehouse) that creates issues for summary and aggregate management to support dashboards and prebuilt reports. • Batch data loading, which persists as the market matures and starts to realize that not all data is required for “right time” latency and that some information, being less volatile, does not need to be refreshed as often as more dynamic real-time data elements. • Large numbers of standard reports, thousands per day, requiring SQL tuning, index creation, new types of storage partitioning and other types of optimization structure in the data warehouse. • Tactical business analytics in which business process professionals with limited query language experience use prebuilt analytic data objects with aggregated data (prejoins) and designated dimensional drill-downs (summaries). They rely on a BI architect to develop commonly used cubes or tables. • An increasing number of truly ad hoc query users (data miners) with random, unpredictable uses of data, which implies a lack of ability to tune specifically for these queries. • The use of analytics and BI-oriented functionality in OLTP applications, creating a highly tactical use of the data warehouse as a source of informa- tion for OLTP applications requiring high-performance queries. This is one force driving the requirement for high availability in the data ware- house. Note 3 Note 4 Definition of Extreme Information Definition of Mission-Critical Systems Issues of “extreme information” arise from the simultaneous and Mission-critical systems are systems that support business processes persistent interaction of extreme volume, diversity of data format, and the generation of revenue and that, if absent for a period of time velocity of record creation, variable latencies and the complexity determined by the organization and its service-level agreements, must of individual data types within formats. Big data is another popular be replaced by manual procedures to prevent loss of revenue or term for this concept, but it encourages a focus on a single aspect unacceptable increases in business costs. Normally, mission-critical (volume), creating definitional issues, which will remain unresolved systems require high-availability systems and disaster recovery sites. in the market (see “’Big Data’ Is Only the Beginning of Extreme We include the use of a DBMS as a data warehouse engine in the Information Management”). mission-critical systems category, as we believe that many, if not most, data warehouses in use today fit the definition of mission-critical. 31
- 32. Evaluation Criteria Definitions Abilityto Execute Product/Service: Core goods and services offered by the vendor that compete in/serve the defined market. This includes current product/ service capabilities, quality, feature sets, skills, etc., whether offered natively or through OEM agreements/partnerships as defined in the market definition and detailed in the subcriteria. Overall Viability (Business Unit, Financial, Strategy, Organization): Viability includes an assessment of the overall organization’s financial health, the financial and practical success of the business unit, and the likelihood of the individual business unit to continue investing in the product, to continue offering the product and to advance the state of the art within the organization’s portfolio of products. Sales Execution/Pricing: The vendor’s capabilities in all pre-sales activities and the structure that supports them. This includes deal management, pricing and negotiation, pre-sales support and the overall effectiveness of the sales channel. Market Responsiveness and Track Record: Ability to respond, change direction, be flexible and achieve competitive success as opportunities develop, competitors act, customer needs evolve and market dynamics change. This criterion also considers the vendor’s history of responsiveness. Marketing Execution: The clarity, quality, creativity and efficacy of programs designed to deliver the organization’s message in order to influence the market, promote the brand and business, increase awareness of the products, and establish a positive identification with the product/brand and organization in the minds of buyers. This “mind share” can be driven by a combination of publicity, promotional, thought leadership, word-of-mouth and sales activities. Customer Experience: Relationships, products and services/programs that enable clients to be successful with the products evaluated. Specifically, this includes the ways customers receive technical support or account support. This can also include ancillary tools, customer support programs (and the quality thereof), availability of user groups, service-level agreements, etc. Operations: The ability of the organization to meet its goals and commitments. Factors include the quality of the organizational structure including skills, experiences, programs, systems and other vehicles that enable the organization to operate effectively and efficiently on an ongoing basis. Completeness of Vision Market Understanding: Ability of the vendor to understand buyers’ wants and needs and to translate those into products and services. Vendors that show the highest degree of vision listen and understand buyers’ wants and needs, and can shape or enhance those with their added vision. Marketing Strategy: A clear, differentiated set of messages consistently communicated throughout the organization and externalized through the website, advertising, customer programs and positioning statements. Sales Strategy: The strategy for selling product that uses the appropriate network of direct and indirect sales, marketing, service and communication affiliates that extend the scope and depth of market reach, skills, expertise, technologies, services and the customer base. Offering (Product) Strategy: The vendor’s approach to product development and delivery that emphasizes differentiation, functionality, methodology and feature set as they map to current and future requirements. Business Model: The soundness and logic of the vendor’s underlying business proposition. Vertical/Industry Strategy: The vendor’s strategy to direct resources, skills and offerings to meet the specific needs of individual market segments, including verticals. Innovation: Direct, related, complementary and synergistic layouts of resources, expertise or capital for investment, consolidation, defensive or pre-emptive purposes. Geographic Strategy: The vendor’s strategy to direct resources, skills and offerings to meet the specific needs of geographies outside the “home” or native geography, either directly or through partners, channels and subsidiaries as appropriate for that geography and market. 32
- 33. About Sybase Sybase, anSAP company, is the industry leader in delivering enterprise and mobile software to manage, analyze and mobilize information. We are recognized globally as a performance leader, proven in the most data-intensive industries and across all major systems, networks and devices. Our information management, analytics and enterprise mobility solutions have powered the world’s most mission-critical systems in financial services, telecommunications, manufacturing and government. For more information, visit www.sybase.com. Read Sybase blogs: blogs.sybase.com. Follow us on Twitter at @Sybase. 33 L03259