1. Top 5 Factors to Consider When
Choosing a Big Data Solution
Robin Schumacher, VP Products
©2012 DataStax 1

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
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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
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4. What big data is and the
domains of data that need to
be considered.
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5. ©2012 DataStax 5

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 doesn't 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…
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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.)
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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.
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9. What are the top five things to
consider in a big data
solution?
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10. ©2012 DataStax 10

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.)
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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
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13. • Includes structured, semi, and unstructured
• Necessitates new data model and file formats
• Involves, real-time, analytic, and search data
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14. • TB’s to PB’s
• Also involves data maintenance functions (e.g.
purging, etc.)
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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)
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16. • Typically involves data distribution, movement,
etc., across multiple data centers and
geographies
• Can be on-premise, cloud, or hybrid
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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.
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18. NoSQL, Cassandra, and
DataStax Enterprise for big
data.
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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
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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
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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
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22. * Uses Cassandra and Hadoop for data management
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23. Cassandra is:
Nearly 4x better in writes
Nearly 2x better in reads
Over 12x better in reads/updates
YCSB Benchmark
Source: http://blog.cubrid.org/dev-platform/nosql-benchmarking/?utm_source=NoSQL+Weekly+List&utm_campaign=143fae86b2-
NoSQL_Weekly_Issue_41_September_8_2011&utm_medium=email
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24. Stores financial options tick data into very fluid data model for storage and
analysis into Cassandra.
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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
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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
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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
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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
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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)?
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30. DataStax Enterprise is tailor made for high-velocity, multi-variety, large
volume, and complex deployment use cases that involve big data.
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31. Recommended Reading
http://www.datastax.com/resources/whitepapers
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32. Next Steps
Download DataStax Enterprise and try it in your
own environment.
› Go to www.datastax.com/
software
› Download a copy of DataStax
Enterprise
› Installs and configures in
minutes
› Completely free for
development use
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33. For More Information
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34. Move Faster.
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