DataStax & 451 Group Webinar - Real NoSQL Applications in the Enterprise Today

1. Dec, 7 2011 Real NoSQL Applications in the Enterprise Today. Apache  Cassandra Jonathan Ellis, CTO DataStax Matt Aslett, 451 Group

2. Welcome and Housekeeping   We will email the presentation after the webinar   Please ask questions using the Q&A panel. I will ask the panelists at the end of the presentation.   You can contact me at mweir@datastax.com

3. Our presenters   Matt Aslett - Senior Analyst   Jonathan Ellis – CTO 451 Group DataStax Matthew covers data Jonathan is CTO and co-founder management software for The at DataStax. Prior to DataStax, 451 Group's Information Jonathan worked extensively Management practice, including with Apache Cassandra while relational and non-relational employed at Racksace. Prior to databases, data warehousing Rackspace, Jonathan built a and data caching. Matthew is multi-petabyte, scalable storage also an expert in open source system based on Reed-Solomon software and contributes encoding for backup provider regularly to reports produced Mozy. In addition to his work with through the 451 Commercial DataStax, Jonathan is project Adoption of Open Source chair of Apache Cassandra. (CAOS) Research Service, as well as to the 451 CAOS Theory blog.

4. The 451 Group 451 Research is focused on the business of enterprise IT innovaAon. The company’s analysts provide criAcal and Amely insight into the compeAAve dynamics of innovaAon in emerging technology segments. Tier1 Research is a single-‐source research and advisory firm covering the mulA-‐tenant datacenter, hosAng, IT and cloud-‐compuAng sectors, blending the best of industry and financial research. The UpAme InsAtute is ‘ The Global Data Center Authority’ and a pioneer in the creaAon and facilitaAon of end-‐user knowledge communiAes to improve reliability and uninterrupAble availability in datacenter faciliAes. TheInfoPro is a leading IT advisory and research firm that provides real-‐world perspecAves on the customer and market dynamics of the enterprise informaAon technology landscape, harnessing the collecAve knowledge and insight of leading IT organizaAons worldwide. ChangeWave Research is a research firm that idenAfies and quanAfies ‘change’ in consumer spending behavior, corporate purchasing, and industry, company and technology trends. © 2011 by The 451 Group. All rights reserved

6. Relevant reports   NoSQL, NewSQL and Beyond  Assessing the drivers behind the development and adopAon of NoSQL and NewSQL databases, as well as data grid/ caching technologies  Released April 2011  Role of open source in driving innovaAon  sales@the451group.com © 2011 by The 451 Group. All rights reserved

7. NoSQL, NewSQL and Beyond NoSQL   New breed of non-‐relaAonal database products   RejecAon of ﬁxed table schema and join operaAons   Designed to meet scalability requirements of distributed architectures   And/or schema-‐less data management requirements © 2011 by The 451 Group. All rights reserved

8. NoSQL, NewSQL and Beyond NoSQL NewSQL   New breed of non-‐relaAonal  New breed of relaAonal database products database products   RejecAon of ﬁxed table schema  Retain SQL and ACID and join operaAons  Designed to meet scalability   Designed to meet scalability requirements of distributed requirements of distributed architectures architectures  Or improve performance so   And/or schema-‐less data horizontal scalability is no management requirements longer a necessity © 2011 by The 451 Group. All rights reserved

9. NoSQL, NewSQL and Beyond NoSQL NewSQL   New breed of non-‐relaAonal  New breed of relaAonal database products database products   RejecAon of ﬁxed table schema  Retain SQL and ACID and join operaAons  Designed to meet scalability   Designed to meet scalability requirements of distributed requirements of distributed architectures architectures  Or improve performance so   And/or schema-‐less data horizontal scalability is no management requirements longer a necessity … and Beyond  In-‐memory data grid/cache products  PotenAal primary pla`orm for distributed data management © 2011 by The 451 Group. All rights reserved

10. NoSQL, NewSQL and Beyond NoSQL   Big tables – data mapped by row key, column key and Ame stamp   Key-‐value stores -‐ store keys and associated values   Document store -‐ stores all data as a single document   Graph databases -‐ use nodes, properAes and edges to store data and the relaAonships between enAAes © 2011 by The 451 Group. All rights reserved

11. NoSQL, NewSQL and Beyond NoSQL NewSQL   Big tables – data mapped by row   MySQL storage engines -‐ scale-‐ key, column key and Ame stamp up and scale-‐out   Key-‐value stores -‐ store keys and   Transparent sharding -‐ reduce to associated values manual eﬀort required to scale   Document store -‐ stores all data as   Appliances -‐ take advantage of a single document improved hardware   Graph databases -‐ use nodes, performance, solid state drives properAes and edges to store data   New databases -‐ designed and the relaAonships between speciﬁcally for scale-‐out enAAes © 2011 by The 451 Group. All rights reserved

12. NoSQL, NewSQL and Beyond NoSQL NewSQL   Big tables – data mapped by row   MySQL storage engines -‐ scale-‐ key, column key and Ame stamp up and scale-‐out   Key-‐value stores -‐ store keys and   Transparent sharding -‐ reduce to associated values manual eﬀort required to scale   Document store -‐ stores all data as   Appliances -‐ take advantage of a single document improved hardware   Graph databases -‐ use nodes, performance, solid state drives properAes and edges to store data   New databases -‐ designed and the relaAonships between speciﬁcally for scale-‐out enAAes Data grid/cache   spectrum of data management capabiliAes, from non-‐persistent data caching to persistent caching, replicaAon, and distributed data and compute grid © 2011 by The 451 Group. All rights reserved

14. SPRAIN Scalability -‐ Hardware economics   Example project/service/vendor: •  BigTable, HBase, Riak, MongoDB, Couchbase, Hadoop, Cassandra •  Amazon RDS, Xeround, SQL Azure, NuoDB •  Data grid/cache   Associated use case: •  Large-‐scale distributed data storage •  Analysis of conAnuously updated data •  MulA-‐tenant PaaS data layer © 2011 by The 451 Group. All rights reserved

15. SPRAIN Performance -‐ MySQL limitaAons   Example project/service/vendor: •  Hypertable, Couchbase, Riak, Membrain, MongoDB, Redis •  Data grid/cache •  VoltDB, Clustrix   Associated use case: •  Real Ame data processing of mixed read/write workloads •  Data caching •  Large-‐scale data ingesAon © 2011 by The 451 Group. All rights reserved

16. SPRAIN Relaxed consistency -‐ CAP Theorem   Example project/service/vendor: •  Dynamo, Voldemort, Cassandra, Riak •  Amazon SimpleDB   Associated use case: •  MulA-‐data center replicaAon •  Service availability •  Non-‐transacAonal data oﬀ-‐load © 2011 by The 451 Group. All rights reserved

17. SPRAIN Agility -‐ polyglot persistence, schema-‐less   Example project/service/vendor: •  MongoDB, CouchDB, Cassandra, Riak •  Google App Engine, SimpleDB, SQL Azure   Associated use case: •  Mobile/remote device synchronizaAon •  Agile development •  Data caching © 2011 by The 451 Group. All rights reserved

18. SPRAIN Intricacy -‐ big data, total data   Example project/service/vendor: •  Neo4j, GraphDB, InﬁniteGraph •  Apache Cassandra, Hadoop, Riak •  VoltDB, Clustrix   Associated use case: •  Social networking applicaAons •  Geo-‐locaAonal applicaAons •  ConﬁguraAon management database © 2011 by The 451 Group. All rights reserved

19. SPRAIN Necessity -‐ open source   The failure of exisAng suppliers to address emerging requirements   Example projects: •  BigTable: Google •  Dynamo: Amazon •  Cassandra: Facebook •  HBase: Powerset •  Voldemort: LinkedIn •  Hypertable: Zvents •  Neo4j: Windh Technologies © 2011 by The 451 Group. All rights reserved

28. Real NoSQL Applications in the Enterprise Today. APACHE CASSANDRA JONATHAN ELLIS 2 8

29. Today’s Database Challenge

30. Navigating the NoSQL waters   Distributed   Horizontally scalable   Eventually consistent   Non-relational   Column store   Document stores   Key-value   Graph   … and more

31. Cassandra: the best for “big data”   Elegant architecture   Operational ﬂexibility   Industry-leading performance   Youshould be using Cassandra for applications requiring   high-performance, realtime queries   scalability past one machine   bulletproof reliability

32. Bigtable, 2006 Dynamo, 2007 OSS, 2008 Incubator, 2009 TLP, 2010 1.0, October 2011

33. Cassandra Highlights   Multi-master, multi-DC   Linearly scalable   Larger-than-memory datasets   High performance   Full durability   Integrated caching   Tuneable consistency

34. Performance A single four-core machine; one million inserts + one million updates

35. The Cassandra Difference Scalable Operational Cost Performance Ease Effective Cassandra * ✔ ✔ ✔ Oracle Exadata ✔ ✔ ✖ MySQL ✖ ✔ ✔ MongoDB ✖ ✔ ✔ Sharding ✔ ✖ ✔ HBase ✔ ✖ ✔ * And when it comes to Performance, we’re unmatched.

36. Why Businesses Choose Cassandra Vertical Big-Data Never Very Easy to Non- Flexible Multi- Cost Scale Down Fast Operate Structured Schema DC / Effective Data Cloud Media / Advertising ✔ ✔ ✔ ✔ ✔ ✔ ✔ Telecomm ✔ ✔ ✔ ✔ ✔ ✔ ✔ Financial ✔ ✔ ✔ ✔ ✔ ✔ Social ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ IT (DaaS) ✔ ✔ ✔ ✔ ✔ ✔ ✔ ✔ Healthcare ✔ ✔ ✔ ✔ ✔ Online Retail ✔ ✔ ✔ ✔ ✔ ✔ The most popular types of applications that use Cassandra are those that… • Are web/SaaS-based, and/or • Collect high volumes of “Data Exhaust” from machine-generated sources

37. “With Cassandra, we get better business agility, and we don’t have to plan capacity in advance, we don’t need to ask permission of other people to build things for us, and we don’t worry about running out of space or power.”  Adrian Cockcroft, Cloud Architect

38. Netﬂix’s problems   Could not build datacenters fast enough   Made decision to go to cloud (AWS)   Cassandra on AWS is a key infrastructure component of its globally distributed streaming product.   Applications include Netﬂix’s subscriber system, AB testing, and viewing history service (including positions at which members stopped watching a streaming program).

39. Netﬂix on Cassandra   Fast   Cheap   Scalable   Flexible   No SPOF

40. “Without Cassandra, our engineers would’ve had to create something that could scale to our needs, that would’ve prevented us from focusing on building product and solving problems for Backupify’s users, which are far more important tasks.”  Matt Conway, VP Engineering

41. Backupify’s problem   Cloud-based utility that enables businesses and consumers to backup, search and restore the content of popular online applications such as Google Apps, Gmail, Facebook, Twitter, and Blogger   Needs:   Horizontal scaling   Ability to handle high write loads   Elasticity with no manual sharding

42. Backupify on Cassandra   Ease of scale enabled engineers to focus on building great applications   DataStax OpsCenter made it easy to monitor the health and perf of their cluster   Reliable, redundant and scalable low- balance data storage helped eliminate down-time   Ability to offer both backup and storage, but also analysis of data eventually

43. “You can seamlessly add new nodes and expand your total capacity without deteriorating the performance of the data store. Cassandra has allowed us to scale very effectively.”  Harry Robertson, Tech Lead

44. Ooyala’s problem   Ooyala provides a suite of technologies and services that support content owners in managing, analyzing and monetizing the digital video they publish online   Needs:   Elasticity,to respond to spikes in data scale   Ability to respond to increasingly sophisticated analytic needs of customers

45. Ooyala on Cassandra   Classic “Big Data” problem did not require re-architecting   Application agility was enabled – developers spend time building cool apps, not ﬁguring out how to scale   Enabled more powerful and granular analytics to their customers

46. “Cassandra has allowed us to build bigger features faster and more reliably, while using less money and without needing to expand our staff.”  Kyle Ambroff, Sr. Engineer

47. Formspring’ problem   Usersof Formspring engage with and learn more about each other by asking and responding to questions. With close to 4B responses in the system and 30M unique users, they needed:   To support explosive growth   To seamlessly syndicate user content   To avoid sharding   Application ﬂexiblity

48. Formspring on Cassandra   No sharding needed – just add nodes to scale   Performance – the popular users with many followers saw no speed reduction.   No more memcached!   Flexibility of a schema-optional architecture is very developer friendly

49. Why DataStax? DataStax delivers database products and services based on Apache Cassandra from experts who are at the forefront of today's data revolution. Database Software & Tools Support & Services   DataStax Enterprise   Production Support   DataStax Community   Consultative Help   DataStax OpsCenter   Professional Training   Drivers & Connectors   Online Documentation

50. DataStax Overview   Founded in April 2010   Commercial leader in Apache Cassandra™, the popular open-source “big data” database   Headquartered in San Francisco Bay area   100+ customers   35+ employees (split between San Fran and Austin)   Home to Apache Cassandra Chair & most committers   Secured $11M in Series B funding in Sep 2011

51. 100+ customers

52. DataStax Value   The simplest way to get started with Apache Cassandra: DataStax Community Edition   A smart, integrated platform that provides Analytics and Real-Time capabilities in the same database, without any resource contention: DataStax Enterprise   The backing of the Cassandra Experts

53. DataStax Enterprise 1.  DataStax Enterprise Database Server 2.  OpsCenter Enterprise Management solution 3.  Expert production support & consultative services

54. Enterprise Database Server Enterprise-class database built to handle today’s big-data needs in a cost-effective, easy, and reliable way.   Leverages resources on-premise or in the cloud   Guarantees uptime with a master-less distributed architecture   Allows for fast application changes via ﬂexible schemas 2 3   Handles structured, semi-structured, and Real-Time unstructured data Replication 1 4   Provides advanced security   Eliminates the need for separate analytics Analytics system 6 5

55. OpsCenter Enterprise OpsCenter Enterprise supplies management, monitoring, and control over DataStax Enterprise   Visual, browser-based user   Proactive alerts that warn interface of impending issues   Administration tasks   Built-in external carried out in point-and- notiﬁcation abilities click fashion   Allows for visual rebalance of data across a cluster when new nodes are added

56. Expert Production Support DataStax Enterprise includes production support and consultative services from the Cassandra experts.   Support service level agreements that range from business hours to 24x7x365   Consultative support for assistance on architecture, design, and tuning   Certiﬁed quarterly service packs   Hot-ﬁx support

57. DataStax Enterprise Compared Scalable Operational Cost Real-Time + Performance Ease Effective Analytics DataStax Enterprise ✔ ✔ ✔ ✔ Oracle Exadata ✔ ✔ ✖ ✔ MySQL ✖ ✔ ✔ ✖ MongoDB ✖ ✔ ✔ ✖ Sharding ✔ ✖ ✔ ✖ HBase ✔ ✖ ✔ ✖ Oracle NoSQL DB ✔ ✖ ? ✔

58. DataStax – Your One-Stop Shop   DataStax Enterprise and Community Editions   Professional Training, Expert Consulting   Documentation and Dev Center   http://www.datastax.com/docs   http://www.datastax.com/dev   Whitepapers, Case Studies, FAQ’s and more   http://www.datastax.com/resources/whitepapers   http://www.datastax.com/resources/casestudies Thank you!

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