A column-oriented DBMS is a database management system (DBMS) that stores its content by column rather than by row. This has advantages for data warehouses and library catalogues where aggregates are computed over large numbers of similar data items.
This talk at the Percona Live MySQL Conference and Expo describes open source column stores and compares their capabilities, correctness and performance.
In these slides we introduce Column-Oriented Stores. We deeply analyze Google BigTable. We discuss about features, data model, architecture, components and its implementation. In the second part we discuss all the major open source implementation for column-oriented databases.
This talk at the Percona Live MySQL Conference and Expo describes open source column stores and compares their capabilities, correctness and performance.
In these slides we introduce Column-Oriented Stores. We deeply analyze Google BigTable. We discuss about features, data model, architecture, components and its implementation. In the second part we discuss all the major open source implementation for column-oriented databases.
A corrected comparison between the databases by Tyler Weatherby 2017 Spring. A benchmark is done between MySQL MyISAM engine, MySQL Memory engine, and MonetDB engine on TPC-H data. In this project, we added the index/key to important tables.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
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
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceCloudera, Inc.
Most developers are familiar with the topic of “database design”. In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
datastage training | datastage online training | datastage training videos | ...Nancy Thomas
website : http://www.todaycourses.com
INTRODUCTION TO DATAWARE HOUSE (DW)
DATA WAREHOUSINGFUNDAMENTALS
Introduction of Data warehousing
purpose of Data warehouse
DW Architecture
Data warehouse vs. OLTP Applications
Data Mart
Data warehouse Lifecycle
DATA MODELING
Introduction of Data Modeling
Entity Relationship(ER) Model
Fact and Dimension Tables
Logical Modeling
Physical Modeling
Schemas – Star Schema & Snowflake Schemas
Fact less Fact Tables
PROCESS – ETL (EXTRACTION, TRANSACTION & LOAD)
Introduction of Extraction , Transformation and Loading (ETL)
Types of available ETL Tools
Key tools in the ETL market
INSTALLATION PROCESS (Datastage)
Windows server
Oracle
.NET
Datastage 7.5X2 & 8x
DIFFERENCE
Server jobs & Parallel jobs
COMPONENTS IN DATASTAGE
Administrator client
Designer client
Director client
Import/export manager
Multi client manager
Console for IBM Information server
Web console for IBM information server
datastage training, datastage online training, datastage training videos, datastage training online, learn datastage online, datastage tutorials for beginners, online datastage training, datastage online training courses, datastage administrator online training, datastage online training, best online datastage training, ibm datastage online training, datastage online training videos, datastage online training in chennai, best datastage training, datawarehousing online training, datastage demo videos
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
A corrected comparison between the databases by Tyler Weatherby 2017 Spring. A benchmark is done between MySQL MyISAM engine, MySQL Memory engine, and MonetDB engine on TPC-H data. In this project, we added the index/key to important tables.
The Information Technology have led us into an era where the production, sharing and use of information are now part of everyday life and of which we are often unaware actors almost: it is now almost inevitable not leave a digital trail of many of the actions we do every day; for example, by digital content such as photos, videos, blog posts and everything that revolves around the social networks (Facebook and Twitter in particular). Added to this is that with the "internet of things", we see an increase in devices such as watches, bracelets, thermostats and many other items that are able to connect to the network and therefore generate large data streams. This explosion of data justifies the birth, in the world of the term Big Data: it indicates the data produced in large quantities, with remarkable speed and in different formats, which requires processing technologies and resources that go far beyond the conventional systems management and storage of data. It is immediately clear that, 1) models of data storage based on the relational model, and 2) processing systems based on stored procedures and computations on grids are not applicable in these contexts. As regards the point 1, the RDBMS, widely used for a great variety of applications, have some problems when the amount of data grows beyond certain limits. The scalability and cost of implementation are only a part of the disadvantages: very often, in fact, when there is opposite to the management of big data, also the variability, or the lack of a fixed structure, represents a significant problem. This has given a boost to the development of the NoSQL database. The website NoSQL Databases defines NoSQL databases such as "Next Generation Databases mostly addressing some of the points: being non-relational, distributed, open source and horizontally scalable." These databases are: distributed, open source, scalable horizontally, without a predetermined pattern (key-value, column-oriented, document-based and graph-based), easily replicable, devoid of the ACID and can handle large amounts of data. These databases are integrated or integrated with processing tools based on the MapReduce paradigm proposed by Google in 2009. MapReduce with the open source Hadoop framework represent the new model for distributed processing of large amounts of data that goes to supplant techniques based on stored procedures and computational grids (step 2). The relational model taught courses in basic database design, has many limitations compared to the demands posed by new applications based on Big Data and NoSQL databases that use to store data and MapReduce to process large amounts of data.
Course Website http://pbdmng.datatoknowledge.it/
Contact me for other informations and to download
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
HBaseCon 2012 | HBase Schema Design - Ian Varley, SalesforceCloudera, Inc.
Most developers are familiar with the topic of “database design”. In the relational world, normalization is the name of the game. How do things change when you’re working with a scalable, distributed, non-SQL database like HBase? This talk will cover the basics of HBase schema design at a high level and give several common patterns and examples of real-world schemas to solve interesting problems. The storage and data access architecture of HBase (row keys, column families, etc.) will be explained, along with the pros and cons of different schema decisions.
datastage training | datastage online training | datastage training videos | ...Nancy Thomas
website : http://www.todaycourses.com
INTRODUCTION TO DATAWARE HOUSE (DW)
DATA WAREHOUSINGFUNDAMENTALS
Introduction of Data warehousing
purpose of Data warehouse
DW Architecture
Data warehouse vs. OLTP Applications
Data Mart
Data warehouse Lifecycle
DATA MODELING
Introduction of Data Modeling
Entity Relationship(ER) Model
Fact and Dimension Tables
Logical Modeling
Physical Modeling
Schemas – Star Schema & Snowflake Schemas
Fact less Fact Tables
PROCESS – ETL (EXTRACTION, TRANSACTION & LOAD)
Introduction of Extraction , Transformation and Loading (ETL)
Types of available ETL Tools
Key tools in the ETL market
INSTALLATION PROCESS (Datastage)
Windows server
Oracle
.NET
Datastage 7.5X2 & 8x
DIFFERENCE
Server jobs & Parallel jobs
COMPONENTS IN DATASTAGE
Administrator client
Designer client
Director client
Import/export manager
Multi client manager
Console for IBM Information server
Web console for IBM information server
datastage training, datastage online training, datastage training videos, datastage training online, learn datastage online, datastage tutorials for beginners, online datastage training, datastage online training courses, datastage administrator online training, datastage online training, best online datastage training, ibm datastage online training, datastage online training videos, datastage online training in chennai, best datastage training, datawarehousing online training, datastage demo videos
“not only SQL.”
NoSQL databases are databases store data in a format other than relational tables.
NoSQL databases or non-relational databases don’t store relationship data well.
A quick tour in 16 slides of Amazon's Redshift clustered, massively parallel database.
Find out what differentiates it from the other database products Amazon has, including SimpleDB, DynamoDB and RDS (MySQL, SQL Server and Oracle).
Learn how it stores data on disk in a columnar format and how this relates to performance and interesting compression techniques.
Contrast the difference between Redshift and a MySQL instance and discover how the clustered architecture may help to dramatically reduce query time.
The design and implementation of modern column oriented databasesTilak Patidar
An attempt to break down the paper on the design of column-oriented databases into simpler terms.
https://stratos.seas.harvard.edu/files/stratos/files/columnstoresfntdbs.pdf
https://blog.acolyer.org/2018/09/26/the-design-and-implementation-of-modern-column-oriented-database-systems/
AWS June 2016 Webinar Series - Amazon Redshift or Big Data AnalyticsAmazon Web Services
Analyzing big data quickly and efficiently requires a data warehouse optimized to handle and scale for large datasets. Amazon Redshift is a fast, petabyte-scale data warehouse that makes it simple and cost-effective to analyze big data for a fraction of the cost of traditional data warehouses. By following a few best practices, you can take advantage of Amazon Redshift’s columnar technology and parallel processing capabilities to minimize I/O and deliver high throughput and query performance. This webinar will cover techniques to load data efficiently, design optimal schemas, and tune query and database performance.
Learning Objectives:
Get an inside look at Amazon Redshift's columnar technology and parallel processing capabilities
Learn how to migrate from existing data warehouses, optimize schemas, and load data efficiently
Learn best practices for managing workload, tuning your queries, and using Amazon Redshift's interleaved sorting features
Take an in-depth look at data warehousing with Amazon Redshift and get answers to your technical questions. We will cover performance tuning techniques that take advantage of Amazon Redshift's columnar technology and massively parallel processing architecture. We will also discuss best practices for migrating from existing data warehouses, optimizing your schema, loading data efficiently, and using work load management and interleaved sorting.
Optimizing Query is very important to improve the performance of the database. Analyse query using query execution plan, create cluster index and non-cluster index and create indexed views
Use a data parallel approach to process large volumes of data (typically terabytes or petabytes) known as big data.
Focus on reliability and availability of data
Cloud Strategies for Financial Firms : Migrating one step at a timeSuvradeep Rudra
A very few financial firms are currently using cloud computing for their core applications, different hosting architectures provided by IaaS cloud providers and new avenues in the community and hybrid cloud space, will drive more firms to move their core applications to the cloud. In fact, core solutions, such as batch processes running throughout the day, analytics and reporting applications, are perfect candidates.
The idea behind a design patterns is to learn about it's strengths and weaknesses. And more importantly, understand where and how to use a particular design correctly, so as to use its strengths properly and overcome its weaknesses.
In today's competitive market, many organizations are unaware of the quantity of poor-quality data in their systems. Some organizations assume that their data is of adequate quality, although they have conducted no metrical or statistical analysis to support the assumption. Others know that their performance is hampered by poor-quality data, but they cannot measure the problem.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
2. Columnar Databases Overview
• A column-oriented DBMS is a database management system (DBMS) that stores its content by
column rather than by row. This has advantages for data warehouses and library catalogues where
aggregates are computed over large numbers of similar data items.
• A column-oriented database serializes all of the values of a column together, then the values of the
next column, and so on. In a column-oriented database, only the columns in the query need to be
retrieved.
• Advantage of column oriented databases over row oriented databases is in the efficiency of hard-
disk access.
3. Disadvantage of a Row Based Database
• In a RDBMS, data values are collected and managed as individual rows and events containing
related rows.
• A row-oriented database must read the entire record or “row” in order to access the needed
attributes or column data.
• Queries most often end up reading significantly more data than is needed to satisfy the request and
it creates very large I/O burdens.
• Architects and DBAs often tune the environment for the different queries by building additional
indexes, pre-aggregating data, and creating special materialized views and cubes. Resulted
in more processing time and consume additional persistent data storage.
• Most of the tuning are query-specific, these tunings only address the performance of the queries
that are known, and do not even touch upon general performance of ad hoc queries.
4. One difference that Matters Most
The major significant difference between columnar and row-based stores
is that all the columns of a table are not stored successively in storage – in
the data pages. This eliminates much of the metadata that is stored on a
data page, which helps the data management component of the DBMS
navigate the many columns in a row-based database as quickly as it can.
In a relational, row-based page, there is a map of offsets near the end of
the page to where the records start on the page. This map is updated as
records come and go on the page. The offset number is also an important
part of how an index entry would find the rest of the record in the data
page in a row-based system. The need for indexes is greatly minimized in
column-based systems, to the point of not being offered in many columnar
Databases.
5. Columnar Database
• A column-oriented database has its data organized and stored by columns.
• System can evaluate which columns are being accessed and retrieve only the values requested
from the specific columns.
• The data values themselves within each column form the index, reducing I/O, enabling rapid
access.
6. Food for Thought !!!!
Think about it from an efficiency standpoint. When I want just a few songs
from an album, it’s cheaper to purchase only those songs from iTunes that
I want. When I want most of the songs, I will save a couple bucks by
purchasing the whole album. Over time, I may find that I like one of those
songs. However, when it comes to a query, the query either wants a
column or it doesn’t. It will not come to like a column later that it was
forced to select. This is foundational to the value proposition for columnar
databases…..
7. Benefits of a Columnar Database
• Better analytic performance: row oriented approach allows better performance in running a large
number of simultaneous queries.
• Rapid joins and aggregation: data access streaming along column-oriented data allows for
incrementally computing the results of aggregate functions, which is critical for data warehouse
applications.
• Suitability for compression: Eliminates storage of multiple indexes, views and aggregations, and
facilitates vast improvements in compression.
• Rapid data loading: In a columnar arrangement the system effectively allows one to segregate
storage by column. This means that each column is built in one pass, and stored separately,
allowing the database system to load columns in parallel using multiple threads. Further, related
performance characteristics of join processing built atop a column store is often sufficiently fast
that the load-time joining required to create fact tables is unnecessary, shortening the latency from
receipt of new data to availability for query processing. Finally, since columns are stored separately,
entire table columns can be added and dropped without downing the system, and without the need
to re-tuning the system following the change
8. Challenges
• No one-size-fits-all system.
• Load time: Converting the data source into columnar format can be unbearably slow where
tens or hundreds of gigabytes of data are involved.
• Incremental loads: Incremental loads can be performance problematic.
• Data compression: Some columnar systems greatly compress the source data.
However, uncompressing the data to read it can slow performance.
• Structural limitations: Columnar databases use different techniques to simulate a relational
structure. Some require the same primary key on all tables, meaning the database
hierarchy is limited to two levels. The limits imposed by a particular system may not seem
to matter, but remember that your needs may change tomorrow. Constraints that seem
acceptable now could prevent you from expanding the system in the future.
• Scalability: Columnar databases major advantage is to get good performance on large
databases. However, is there is reasonable to use columnar databases in case you are
dealing with common size database?
9. Best Practices in using Columnar DB
• Use to Save Money on Storage
– All column data stores keep the data in the same row order so that when the records
are pieced together, the correct concatenation of columns is done to make up the row.
– matches values to rows according to the position of the value (i.e., 3rd value in each
column belongs to the 3rd row, etc.). This way “SUVRADEEP” (from the first name
column file2) is matched with “RUDRA” (from the last name column file) correctly –
instead of matching “DAS” with “SUVRADEEP”, for example.
– Dictionary Method - dictionary structure is used to store the actual values along with
tokens .
– The dictionary arrangement allows DB, to trim insignificant trailing nulls from character
fields, furthering the space savings. Effectively, characters over 8 bytes are treated as
variable length characters.
10. For example, 1=State Grid Corporation of China, 2=Nippon Telegraph and
Telephone and 3=Federal Home Loan Mortgage Corporation could be in the
dictionary and when those are the column values, the 1, 2 and 3 are used
in lieu of the actual values. If there are 1,000,000 customers with only 50
possible values, the entire column could be stored with 8 megabytes (8
bytes per value). The separate dictionary structure, containing each
unique value and its associated token, would have more page-level
metadata. Since each value can have a different length, a map to where
the values start on the page would be stored, managed and utilized in
page navigation.
• DICTIONARY: 1, State Grid Corporation of China, 2, Nippon Telegraph and Telephone, 3,
Federal Home Loan Mortgage Corporation
• DATA PAGE: 1,3,2,3,1,3,1, …
11. Speed for Input/output Bound Queries
• Optimized the I/O operation
• In row-based databases, complete file scans mean I/O of data that is non-essential to the query. This non-
essential data could comprise a very large percentage of the I/O.
• Much more of the data in the I/O is essential to a columnar query. An I/O in a columnar database will only
retrieve one column – a column interesting to the particular query from either a selection or projection
(WHERE clause) capacity. The projection function starts first and gathers a list of record numbers to be
returned, which is used with the selection queries (if different from projection) to materialize the result
set.
• Columnar databases can perform full column scans much quicker than a row-based system would turn to a
full table scan. Query time spent in the optimizer is reduced significantly.
• Columnar databases are one of many new approaches taking workloads off the star schema data
warehouse, which is where many of the I/O bound queries are today. Heterogeneity in post-operational
systems is going to be the norm for some time, and columnar databases are a major reason because they
can outperform many of the queries executed in the data warehouse.
12. Beyond Cubes
• Multidimensional databases (MDBs), or cubes, are separate physical structures that support very fast
access to selective data.
• When a query asks for most columns of the MDB, the MDB will perform quite well. The physical storage of
these MDBs is a demoralized dimensional model, which eliminates joins. However, MDBs get large and
grow faster than expected as columns are added. They can also grow in numbers across the
organization, becoming an unintentional impediment to information access.
• The processing step (data load) can be quite lengthy, especially on large data volumes in a MDB.
• Difficulty updating and querying models with more than ten dimensions.
• Traditionally have difficulty querying models with dimensions with very high cardinality
• It is difficult to develop the necessary discipline to use MDBs with its best-fit workloads. MDB abuse is a
major cause of the complete overhaul of the information management environment. Many are looking for
scalable alternatives and the analytic workloads used with MDBs tend to have a lot in common with the
more manageable columnar databases.
13. Not a cup of tea for Hadoop
• Hadoop is a parallel programming framework for large-scale data.
• The ideal workload for Hadoop is data that is massive not only from the standpoint of collecting history over
time, but also from the standpoint of high volume in a single day.
• Hadoop systems are flat file based with no relational database for performance, nearly all queries run a file
scan for every task, even if the answer is found in the first block of disk data.
• Hadoop systems are best suited for unstructured data, for that is the data that amasses large very
quickly, needing only batch processing and a basic set of query capabilities.
• It is not for data warehousing nor the analytic, warehousing-like workloads.
• Therefore ,summary data will be sent from Hadoop (using Sqoop ,Flume, Hive ..etc) to the columnar
database. Analysts and business consumers access the columnar database 7 X 24 for ad-hoc reporting and
analysis, whereas Hadoop access is scheduled and more restricted.
Columnar databases allow you to implement a data model free
of the tuning and massaging that must occur to designs in row-
based databases, such as making the tables unnaturally small to
simulate columnar efficiencies.
*Sqoop - Integration of databases and data warehouses with Hadoop
*Flume - Configurable streaming data collection
*Hive - SQL-like queries and tables on large datasets
14. Conclusion
• Columnar database benefits are enhanced with larger amounts of data,
large scans and I/O bound queries. While providing performance benefits,
they also have unique abilities to compress their data, like cubes, data
warehouses and Hadoop, they are an important component of a modern,
heterogeneous environment. By following these guidelines and moving the
best workloads to columnar databases ,an organization is best enabled to
pursue the full utilization of one of its most important assets - information.
15. References:
• Wikipedia
• David M. Raab , How To Judge A Columnar Database, Information Management
Magazine, 2007
• Lou Agosta, Columnar databases, appliances, cloud computing top BI trends,
SearchDataManagement, 2009
• David Loshin,Gaining the Performance Edge Using a Column-Oriented Database
Management System, Sybase whitepaper
• http://www.wilshireconferences.com/NoSQL2011/WP/Calpont%20Whitepaper.pdf
• http://www.cloudera.com/
Editor's Notes
Rapid joins and aggregation: data access streaming along column-oriented data allows for incrementally computing the results of aggregate functions, which is critical for data warehouse applications. In addition, there is no requirement for different columns of data to be stored together; allocating columnar data across multiple processing units and storage allows for parallel accesses and aggregations as well, increasing the overall query performance.Suitability for compression: The columnar storage of data not only eliminates storage of multiple indexes, views and aggregations, but also facilitates vast improvements in compression, which can result in an additional reduction in storage while maintaining high performance.Rapid data loading: The typical process for loading data into a data warehouse involves extracting data into a staging area, performing transformations, joining data to create demoralized representations and loading the data into the warehouse as fact and dimension tables, and then creating the collection of required indexes and views. In a row-based arrangement, all of the data values in each row need to be stored together, and then indexes must be constructed by reviewing all the row data. In a columnar arrangement the system effectively allows one to segregate storage by column. This means that each column is built in one pass, and stored separately, allowing the database system to load columns in parallel using multiple threads. Further, related performance characteristics of join processing built atop a column store is often sufficiently fast that the load-time joining required to create fact tables is unnecessary, shortening the latency from receipt of new data to availability for query processing. Finally, since columns are stored separately, entire table columns can be added and dropped without downing the system, and without the need to re-tuning the system following the change
Load time, incremental loads, and data compression. These all reflect the need for a columnar database to restructure data originally stored in another format. They can be critical bottlenecks at large data volumes, and older systems varied widely in their performance. As a result, these were probably the most important considerations when comparing older columnar systems.