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Top 5 Considerations for a Big Data Solution


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This presentation suggests the top 5 things architects and IT managers need to look for in a big data solution.

Published in: Technology, Business

Top 5 Considerations for a Big Data Solution

  1. 1. Top 5 Factors to Consider WhenChoosing a Big Data Solution Robin Schumacher, VP Products©2012 DataStax 1
  2. 2. •  VP Products, DataStax •  Director of Product Management MySQL, then EnterpriseDB •  VP Product Management at Embarcadero Technologies •  DBA with Oracle, Teradata, SQL Server, DB2, others… •  Database software reviewer for various magazines •  Author of 3 database books©2012 DataStax 2
  3. 3. •  Define big data •  Identify “must have’s” of a big data solution •  Discuss difficulty in getting all of them from a business and technical perspective •  Brief tour of NoSQL, Cassandra and DataStax Enterprise©2012 DataStax 3
  4. 4. What big data is and the domains of data that need to be considered.©2012 DataStax 4
  5. 5. ©2012 DataStax 5
  6. 6. “Big data technologies describe a new generation of technologies and architectures, designed to economically extract value from very large volumes of a wide variety of data, by enabling high-velocity capture, discovery, and/or analysis.” "Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesnt fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it." ”Datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze " * All definitions have one thing in common: new technology is needed for big data…©2012 DataStax 6
  7. 7. 1.  Real-time – transactional, online, streaming, low latency data 2.  Analytic – aggregated data from real-time feeds or other sources; many times batch in nature 3.  Search – supporting data, both external and internal, used for locating desired information and/or objects (e.g. products, documents, etc.)©2012 DataStax 7
  8. 8. Research done by McKinsey & Company shows the eye-opening, 10-year category growth rate differences between businesses that smartly use their big data and those that do not.©2012 DataStax 8
  9. 9. What are the top five things to consider in a big data solution?©2012 DataStax 9
  10. 10. ©2012 DataStax 10
  11. 11. The characteristics that define big data are: 1.  Velocity – includes the speed at which data comes in, and the number of events/elements being stored 2.  Variety – involves structured, semi-structured, unstructured data 3.  Volume – can equate to TB-PB’s of data 4.  Complexity – typically entails the difficulty distributing the data (e.g. multi-data centers, cloud, etc.) and managing the data traffic/movement (e.g. ETL, migrations, etc.)©2012 DataStax 11
  12. 12. •  Data has high rate of input •  Data has large quantity of elements/events • Sensor data • Media streaming • Mobile devices • Financial streams • Web clickstream • Traffic monitoring • Patient care©2012 DataStax 12
  13. 13. •  Includes structured, semi, and unstructured •  Necessitates new data model and file formats •  Involves, real-time, analytic, and search data©2012 DataStax 13
  14. 14. •  TB’s to PB’s •  Also involves data maintenance functions (e.g. purging, etc.)©2012 DataStax 14
  15. 15. The McKinsey report found that the average investment firm with fewer than 1,000 employees has 3.8 petabytes of data stored, experiences a data growth rate of 40 percent per year, and stores structured, semi-structured, and unstructured data. Overall, McKinsey found that 15 out of 17 industry sectors in the United States have more data stored per company than the U.S. Library of Congress (which had 235 terabytes of information at the time of McKinsey’s study)©2012 DataStax 15
  16. 16. •  Typically involves data distribution, movement, etc., across multiple data centers and geographies •  Can be on-premise, cloud, or hybrid©2012 DataStax 16
  17. 17. Getting a big data technology that provides two out of three can be challenging; finding one that supplies all three can be very hard.©2012 DataStax 17
  18. 18. NoSQL, Cassandra, and DataStax Enterprise for big data.©2012 DataStax 18
  19. 19. NoSQL is a broad class of next-generation database management systems that differ from the classic model of the relational database management system (RDBMS) in some significant ways, most important being they: •  Sport a less-rigid, more dynamic data model •  Look to provide user controlled trade-off’s to the CAP theorem •  Do not support ANSI SQL or operations such as joins •  Attempt to solve some or all of the challenges of big data©2012 DataStax 19
  20. 20. A NoSQL solution like Apache Cassandra: •  Handles high velocity data with ease •  Uses schema that support broad varieties of data •  Scales from GB’s to PB’s with linear performance capabilities •  Is built to handle multi-location/data center use cases •  Is designed for continuous availability •  Offers quick installation and configuration for multi-node clusters •  Is open source and/or cost 80-90% less than RDBMS’s©2012 DataStax 20
  21. 21. Overview of DataStax •  Founded in April 2010 •  Commercial leader in Apache Cassandra™, the popular open-source “big data” database •  140+ customers •  40+ employees •  Home to Apache Cassandra Chair & most committers •  Headquartered in San Francisco Bay area •  Funded by prominent venture firms©2012 DataStax 21
  22. 22. * Uses Cassandra and Hadoop for data management©2012 DataStax 22
  23. 23. Cassandra is: Nearly 4x better in writes Nearly 2x better in reads Over 12x better in reads/updates YCSB Benchmark Source: NoSQL_Weekly_Issue_41_September_8_2011&utm_medium=email©2012 DataStax 23
  24. 24. Stores financial options tick data into very fluid data model for storage and analysis into Cassandra.©2012 DataStax 24
  25. 25. “The hundreds of millions of web pages that contain this information are stored in a multi-terabyte cache that grows continually as we crawl the web, analyzing new pages and finding new versions of existing pages.” – Zoominfo Architect on using Cassandra©2012 DataStax 25
  26. 26. “I can create a Cassandra cluster in any region of the world in 10 minutes. When marketing guys decide we want to move into a certain part of the world, we’re ready.” - Netflix architect©2012 DataStax 26
  27. 27. •  Fully integrated smart big data platform •  Production certified Cassandra •  Continuously available analytics with Hadoop •  Scalable enterprise search with Solr •  Built in workload isolation •  No costly and error-prone ETL operations •  Easy migration of RDBMS and log data •  Simple to install and grow •  OpsCenter management solution •  80-90% less cost than RDBMS vendors©2012 DataStax 27
  28. 28. •  DataStax OpsCenter is a visual management and monitoring solution for DataStax Enterprise •  Manage and monitor all Cassandra and Hadoop and Solr operations •  Visual alerts and notifications©2012 DataStax 28
  29. 29. 1.  Does it handle high data velocity? 2.  Can it tackle all types of data? 3.  How well does it perform with large data volumes? 4.  Can it handle complex distribution and implementation use cases (e.g. on-premise/cloud, multi-geo)? 5.  How does it stack up in hitting the big data “bulls eye?” (i.e. cost, saleable performance, and operational ease are concerned)?©2012 DataStax 29
  30. 30. DataStax Enterprise is tailor made for high-velocity, multi-variety, large volume, and complex deployment use cases that involve big data.©2012 DataStax 30
  31. 31. Recommended Reading©2012 DataStax 31
  32. 32. Next Steps Download DataStax Enterprise and try it in your own environment. ›  Go to software ›  Download a copy of DataStax Enterprise ›  Installs and configures in minutes ›  Completely free for development use©2012 DataStax 32
  33. 33. For More Information©2012 DataStax 33
  34. 34. Move Faster.©2012 DataStax 34