OLAP tools are categorized based on how they store and process multi-dimensional data, with the main categories being MOLAP, ROLAP, HOLAP, and DOLAP. MOLAP uses specialized data structures and MDDBMS to organize and analyze aggregated data for optimal query performance. ROLAP uses relational databases with a metadata layer to facilitate multiple views of data. HOLAP combines aspects of MOLAP and ROLAP. DOLAP provides limited analysis directly from databases or via servers to desktops in the form of datacubes for local storage, analysis and maintenance.
Dated: 19th July 2009
By:Shahzad Sarwar To: Related Project Managers/Consultants,Client
Case Study:
To sync data of different branches of office via replication who are running Comsoft application named PCMS.
Dated: 19th July 2009
By:Shahzad Sarwar To: Related Project Managers/Consultants,Client
Case Study:
To sync data of different branches of office via replication who are running Comsoft application named PCMS.
Backing up Microsoft Great Plains / Microsoft Dynamics GPHandy_Backup
To back up data created or changed by Great Plains, the third-party software utility must have an automated
access to such data types as MS SQL databases and different files and folders containing user data. Handy
Backup provides these features, allowing fully automated Great Plains data backup.
Comparison of Zabbix with market competitorsRodrigo Mohr
In order to identify the ideal monitoring tool to be used in Dell's corporate environment, an analysis of various monitoring tools in the market, including Zabbix, was performed. This presentation aims to highlight the main strengths and weaknesses identified in Zabbix compared to its competitors, based on the needs of our company.
As the core SQL processing engine of the Greenplum Unified Analytics Platform, the Greenplum Database delivers Industry leading performance for Big Data Analytics while scaling linearly on massively parallel processing clusters of standard x86 servers. This session reviews the product's underlying architecture, identify key differentiation areas, go deep into the new features introduced in Greenplum Database Release 4.2, and discuss our plans for 2012.
The combination of Lenovo Storage S3200 arrays and DataCore SANsymphony-V Software-defined Storage platform, certified under the rigorous DataCore Ready
Program, offers a flexible choice of capacity and performance-based storage upgrades while providing an easy method to migrate data from older devices.
White Paper: Backup and Recovery of the EMC Greenplum Data Computing Applian...EMC
This White Paper explores backing up EMC Greenplum Data Computing Appliance data to Data Domain systems and how to effectively exploit Data DomainTs leading-edge technology.
Backing up Microsoft Great Plains / Microsoft Dynamics GPHandy_Backup
To back up data created or changed by Great Plains, the third-party software utility must have an automated
access to such data types as MS SQL databases and different files and folders containing user data. Handy
Backup provides these features, allowing fully automated Great Plains data backup.
Comparison of Zabbix with market competitorsRodrigo Mohr
In order to identify the ideal monitoring tool to be used in Dell's corporate environment, an analysis of various monitoring tools in the market, including Zabbix, was performed. This presentation aims to highlight the main strengths and weaknesses identified in Zabbix compared to its competitors, based on the needs of our company.
As the core SQL processing engine of the Greenplum Unified Analytics Platform, the Greenplum Database delivers Industry leading performance for Big Data Analytics while scaling linearly on massively parallel processing clusters of standard x86 servers. This session reviews the product's underlying architecture, identify key differentiation areas, go deep into the new features introduced in Greenplum Database Release 4.2, and discuss our plans for 2012.
The combination of Lenovo Storage S3200 arrays and DataCore SANsymphony-V Software-defined Storage platform, certified under the rigorous DataCore Ready
Program, offers a flexible choice of capacity and performance-based storage upgrades while providing an easy method to migrate data from older devices.
White Paper: Backup and Recovery of the EMC Greenplum Data Computing Applian...EMC
This White Paper explores backing up EMC Greenplum Data Computing Appliance data to Data Domain systems and how to effectively exploit Data DomainTs leading-edge technology.
Le but est de partager avec le public les connaissances et expériences éprouvées dans la conception, la mise en œuvre et l'exécution de plateformes DBaaS. La présentation comprend des exemples et des explications sur les environnements de base de données consolidées délivrant des performances sans compromis, l'évolutivité et la flexibilité en liaison avec le "time-to-market" et la rentabilité.
Building High Performance MySQL Query Systems and Analytic ApplicationsCalpont
This presentation describes how to build fast running MySQL applications that service read-based systems. It takes a special look at column databases and Calpont's InfiniDB
Building High Performance MySql Query Systems And Analytic Applicationsguest40cda0b
This presentation gives practical advice and tips on how to build high-performance read intensive databases, and discusses innovations such as column-oriented databases
Equnix Business Solutions (Equnix) is an IT Solution provider in Indonesia, providing comprehensive solution services especially on the infrastructure side for corporate business needs based on research and Open Source. Equnix has 3 (three) main services known as the Trilogy of Services: Support (Maintenance/Managed), World class level of Software Development, and Expert Consulting and Assessment for High Performance Transactions System. Equnix is customer oriented, not product or principal. Equal opportunity based on merit is our credo in managing HR development.
Enabling a Data Mesh Architecture with Data VirtualizationDenodo
Watch full webinar here: https://bit.ly/3rwWhyv
The Data Mesh architectural design was first proposed in 2019 by Zhamak Dehghani, principal technology consultant at Thoughtworks, a technology company that is closely associated with the development of distributed agile methodology. A data mesh is a distributed, de-centralized data infrastructure in which multiple autonomous domains manage and expose their own data, called “data products,” to the rest of the organization.
Organizations leverage data mesh architecture when they experience shortcomings in highly centralized architectures, such as the lack domain-specific expertise in data teams, the inflexibility of centralized data repositories in meeting the specific needs of different departments within large organizations, and the slow nature of centralized data infrastructures in provisioning data and responding to changes.
In this session, Pablo Alvarez, Global Director of Product Management at Denodo, explains how data virtualization is your best bet for implementing an effective data mesh architecture.
You will learn:
- How data mesh architecture not only enables better performance and agility, but also self-service data access
- The requirements for “data products” in the data mesh world, and how data virtualization supports them
- How data virtualization enables domains in a data mesh to be truly autonomous
- Why a data lake is not automatically a data mesh
- How to implement a simple, functional data mesh architecture using data virtualization
Learn about IBM FlashSystem in OLAP Database Environments. IBM FlashSystem storage systems deliver high performance and efficiency in an easy to integrate offering so that businesses can more readily compete in the market.FlashSystem storage systems transform the data center environment and enhance performance and resource consolidation to gain the most from business processes and critical applications. For more information on IBM FlashSystem, visit http://ibm.co/10KodHl.
Visit http://bit.ly/KWh5Dx to 'Follow' the official Twitter handle of IBM India Smarter Computing.
Compare and contrast big data processing platforms RDBMS, Hadoop, and Spark. pros and cons of each platform are discussed. Business use cases are also included.
With tremendous growth in big data, low latency and high throughput is the key ask for many big data application. The in-memory technology market is growing rapidly. We see that traditional database vendors are extending their platform to support in-memory capability and others are offering in-memory data grid and NoSQL solutions for high performance and scalability. In this talk, we will share our point of view on In-Memory Data Grid and NoSQL technology. It is all about how to build architecture that meets low latency and high throughput requirements. We will share our thoughts and experiences in implementing the use cases that demands low latency & high throughput with inherent scale-out features.
You will learn about how in-memory data grid and NoSQL is used to meet the low latency and high throughput needs and choosing in-memory technology that is good fit for your use case.
The Top 5 Reasons to Deploy Your Applications on Oracle RACMarkus Michalewicz
A presentation for developers, DBAs, and managers. This presentation was first presented in course of the AIOUG Maximum Availability Architecture (MAA)-focus month August 2021. The first reason might surprise you!
2. OLAP tools are categorized according to the
architecture used to store and process multi-
dimensional data.
There are four main categories:
Multi-dimensional OLAP (MOLAP)
Relational OLAP (ROLAP)
Hybrid OLAP (HOLAP)
Desktop OLAP (DOLAP)
2
3. Use specialized data structures and multi-
dimensional Database Management Systems
(MDDBMSs) to organize, navigate, and
analyze data.
Data is typically aggregated and stored
according to predicted usage to enhance
query performance.
3
4. Use array technology and efficient storage
techniques that minimize the disk space
requirements through sparse data
management.
Provides excellent performance when data is
used as designed, and the focus is on data for
a specific decision-support application.
4
5. Traditionally, require a tight coupling with the
application layer and presentation layer.
Recent trends segregate the OLAP from the
data structures through the use of published
application programming interfaces (APIs).
5
7. MOLAP products require a different set of
skills and tools to build and maintain the
database, thus increasing the cost and
complexity of support.
7
8. Observe la
normalización
de los miembros
Observe el
almacenamiento del
array en disco ó RAM
8
9. Fastest-growing style of OLAP technology
due to requirements to analyze ever-
increasing amounts of data and the
realization that users cannot store all the
data they require in MOLAP databases.
9
10. Supports RDBMS products using a metadata
layer - avoids need to create a static multi-
dimensional data structure - facilitates the
creation of multiple multi-dimensional views
of the two-dimensional relation.
10
11. To improve performance, some products use
SQL engines to support the complexity of
multi-dimensional analysis, while others
recommend, or require, the use of highly
denormalized database designs such as the
star schema.
11
13. Performance problems associated with the
processing of complex queries that require
multiple passes through the relational data.
Middleware to facilitate the development of
multi-dimensional applications. (Software
that converts the two-dimensional relation
into a multi-dimensional structure).
13
14. Provide limited analysis capability, either
directly against RDBMS products, or by using
an intermediate MOLAP server.
Deliver selected data directly from the DBMS
or via a MOLAP server to the desktop (or
local server) in the form of a datacube, where
it is stored, analyzed, and maintained locally.
14
15. Promoted as being relatively simple to install
and administer with reduced cost and
maintenance.
15
17. Architecture results in significant data
redundancy and may cause problems for
networks that support many users.
Ability of each user to build a custom
datacube may cause a lack of data
consistency among users.
Only a limited amount of data can be
efficiently maintained.
17
18. Store the OLAP data in client-based files and
support multi-dimensional processing using a
client multi-dimensional engine.
Requires that relatively small extracts of data
are held on client machines. They may be
distributed in advance, or created on demand
(possibly through the Web).
18
19. As with multi-dimensional databases on the
server, OLAP data may be held on disk or in
RAM, however, some DOLAP products allow
only read access.
Most vendors of DOLAP exploit the power of
desktop PC to perform some, if not most,
multi-dimensional calculations.
19
20. The administration of a DOLAP database is
typically performed by a central server or
processing routine that prepares data cubes
or sets of data for each user.
Once the basic processing is done, each user
can then access their portion of the data.
20
22. Provision of appropriate security controls to
support all parts of the DOLAP environment.
Since the data is physically extracted from
the system, security is generally
implemented by limiting the information
compiled into each cube. Once each cube is
uploaded to the user's desktop, all additional
meta data becomes the property of the local
user.
22
23. Reduction in the effort involved in deploying
and maintaining the DOLAP tools. Some
DOLAP vendors now provide a range of
alternative ways of deploying OLAP data
such as through e-mail, the Web or using
traditional client/server architecture.
Current trends are towards thin client
machines.
23
24. Efraim Turban. Business Intelligence. Prentice
Hall.2008.