Top 5 Considerations for a Big Data Solution
Upcoming SlideShare
Loading in...5
×
 

Top 5 Considerations for a Big Data Solution

on

  • 20,361 views

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

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

Statistics

Views

Total Views
20,361
Views on SlideShare
20,001
Embed Views
360

Actions

Likes
16
Downloads
617
Comments
3

8 Embeds 360

http://www.datastax.com 291
http://www-ig-opensocial.googleusercontent.com 36
http://feeds.feedburner.com 24
http://paper.li 3
http://www.techgig.com 3
http://techgig.in 1
https://twitter.com 1
http://www.linkedin.com 1
More...

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Top 5 Considerations for a Big Data Solution Top 5 Considerations for a Big Data Solution Presentation Transcript

  • Top 5 Factors to Consider WhenChoosing a Big Data Solution Robin Schumacher, VP Products©2012 DataStax 1
  • •  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
  • •  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
  • What big data is and the domains of data that need to be considered.©2012 DataStax 4
  • ©2012 DataStax 5
  • “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
  • 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
  • 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
  • What are the top five things to consider in a big data solution?©2012 DataStax 9
  • ©2012 DataStax 10
  • 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
  • •  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
  • •  Includes structured, semi, and unstructured •  Necessitates new data model and file formats •  Involves, real-time, analytic, and search data©2012 DataStax 13
  • •  TB’s to PB’s •  Also involves data maintenance functions (e.g. purging, etc.)©2012 DataStax 14
  • 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
  • •  Typically involves data distribution, movement, etc., across multiple data centers and geographies •  Can be on-premise, cloud, or hybrid©2012 DataStax 16
  • 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
  • NoSQL, Cassandra, and DataStax Enterprise for big data.©2012 DataStax 18
  • 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
  • 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
  • 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
  • * Uses Cassandra and Hadoop for data management©2012 DataStax 22
  • 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©2012 DataStax 23
  • Stores financial options tick data into very fluid data model for storage and analysis into Cassandra.©2012 DataStax 24
  • “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
  • “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
  • •  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
  • •  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
  • 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
  • DataStax Enterprise is tailor made for high-velocity, multi-variety, large volume, and complex deployment use cases that involve big data.©2012 DataStax 30
  • Recommended Reading http://www.datastax.com/resources/whitepapers©2012 DataStax 31
  • 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©2012 DataStax 32
  • For More Information©2012 DataStax 33
  • Move Faster.©2012 DataStax 34