In the world of web ad and offer targeting, speed and scalability are just about everything. In this webinar, you will learn how the changing application landscape is driving the evolution of data management technology. You will also learn how AOL Advertising leverages Hadoop and Membase NoSQL database technology to rapidly process operational user data to achieve sub-millisecond performance. AOL’s ad serving platforms see billions of impressions daily, and any incremental improvement in processing time translates as largely beneficial in meeting the read/write goal of five milliseconds. AOL uses Cloudera’s Distribution for Apache Hadoop to create user profiles then serves them from Membase. Speakers: Matt Aslett, Senior Analyst, The 451 Group; Pero Subasic, Chief Architect, AOL Sponsors: Cloudera and Membase.

1. Welcome to today’s webinar How AOL Accelerates Ad Targeting Decisions with Hadoop and Membase Server Audio/Telephone: +1 (805) 309-0021 Access Code: 670-793-134 Audio PIN: Shown after joining the Webinar Host: John Kreisa, VP of Marketing, Cloudera

2. Housekeeping Ask questions at any time using the Questions panel Problems? Use the Chat panel Recording will be available

3. About the Webinar How AOL Accelerates Ad Targeting Decisions with Hadoop and Membase Server

4. Speakers Matt Aslett, Senior Analyst, Enterprise Software Matt covers data management software for The 451 Group's Information Management practice, including relational and non-relational databases, data warehousing and data caching. Matthew is also an expert in open source software and contributes regularly to reports produced through the 451 Commercial Adoption of Open Source (CAOS) Research Service, as well as to the 451 CAOS Theory blog. PeroSubasic, Chief Architect, AOL Pero works on research and development in new technologies and contextual advertising at Aol Advertising in Palo Alto. Over the past 4 years he was the Chief Architect of R&D distributed infrastructure which today comprises more than 1000 nodes in multiple data centers. He also led large-scale contextual analysis and segmentation projects and a variety of machine learning efforts at Aol, Yahoo and Cadence Design Systems and published patents and research papers in these areas.

5. NoSQL and Hadoop: Open source innovation and adoption drivers Matthew Aslett, senior analyst The 451 Group

6. Analyzing the business of Enterprise IT Innovation Unique Analysis of the Hosting, Managed Service, Third-Party Datacenter and Internet Infrastructure sectors The 451 Group The Uptime Institute is the leading independent think tank and research body serving the global datacenter industry.

8. Senior analyst, enterprise software

9. With The 451 Group since 2007

10. www.about.me/mattaslett

13. 30.4% using open source databases

14. Main usage areas

15. 45% In-house-developed apps

16. 41% Single-function apps

17. 38% Development

19. Relevant reports Warehouse Optimization Ten considerations for choosing/building a data warehouse Published September 2009 The role of open source and emergence of Hadoop sales@the451group.com

20. Open source database landscape 2009 Analytic Infobright InfiniDB MonetDB LucidDB Hadoop

21. Relevant reports Data Warehousing 2009-2013 Market Sizing, Landscape and Future Published August 2010 The potential impact of Hadoop sales@the451group.com

22. Open source database landscape 2011 Analytic Infobright (InfiniDB) MonetDB LucidDB Hadoop Hadoop Pig Hive ZooKeeper Mahout Avro

23. Open source database landscape 2011 Hadoop Hadoop Pig Hive ZooKeeper Mahout Avro Analytic Infobright (InfiniDB) MonetDB LucidDB Cassandra CouchDB MongoDB NoSQL HBase Membase Riak

24. Relevant reports “Database alternatives” Assessing the drivers behind the development and adoption of NoSQL and scalable SQL databases, as well as Hadoop Planned for April 2011 Role of open source in driving innovation COMING APRIL 2011

25. SPRAINED RELATIONAL DATABASES Photo credit: Foxtongue on Flickr http://www.flickr.com/photos/foxtongue/4844016087/

27. “An injury to ligaments… caused by being stretched beyond normal capacity”Wikipedia

28. SPRAINed relational databases SPRAIN: “An injury to ligaments… caused by being stretched beyond normal capacity” Wikipedia Six key drivers for NoSQL/Hadoop adoption Scalability Performance Relaxed consistency Agility Intricacy Necessity

29. SPRAINed relational databases SPRAIN: “An injury to ligaments… caused by being stretched beyond normal capacity” Wikipedia Six key drivers for NoSQL/Hadoop adoption Scalability Performance – performance does not necessarily mean scalability Relaxed consistency Agility Intricacy Necessity

30. SPRAINed relational databases SPRAIN: “An injury to ligaments… caused by being stretched beyond normal capacity” Wikipedia Six key drivers for NoSQL/Hadoop adoption Scalability Performance – performance does not necessarily mean scalability Relaxed consistency – where scalability is a given Agility Intricacy Necessity

31. SPRAINed relational databases SPRAIN: “An injury to ligaments… caused by being stretched beyond normal capacity” Wikipedia Six key drivers for NoSQL/Hadoop adoption Scalability Performance – performance does not necessarily mean scalability Relaxed consistency – where scalability is a given Agility – flexible, schema-free data models and agile development Intricacy Necessity

32. SPRAINed relational databases SPRAIN: “An injury to ligaments… caused by being stretched beyond normal capacity” Wikipedia Six key drivers for NoSQL/Hadoop adoption Scalability Performance – performance does not necessarily mean scalability Relaxed consistency – where scalability is a given Agility – flexible, schema-free data models and agile development Intricacy – complex relationships and data types Necessity

33. Scalability users application database hardware

34. Scalability users users users application application application database hardware

35. Scalability users users users application application application database hardware hardware hardware hardware hardware hardware hardware hardware

36. Scalability users users users users users users users users application application application database hardware hardware hardware hardware hardware hardware hardware hardware

37. Scalability users users users users users users users users application application application application application application database hardware hardware hardware hardware hardware hardware hardware hardware

38. Scalability users users users users users users users users application application application application application application DATA – large volumes, structured and unstructured, real-time demands database hardware hardware hardware hardware hardware hardware hardware hardware

39. Scalability users users users users users users users users application application application application application application BIG DATA – Volume, Variety and Velocity database hardware hardware hardware hardware hardware hardware hardware hardware

40. Scalability Operational database Database Analytic database

41. Scalability big audience real-timetransactional data management Database large scale data analysis big data

43. real-time

44. low, predictable latency

46. batch processing

47. analytics-optimized

48. data locality modellarge scale data analysis big data

50. real-time

51. low, predictable latency

53. batch processing

54. analytics-optimized

55. data locality modelCloudera’s Distribution for Apache Hadoop big data

56. Scalability big audience Membase Membase Membase Membase Membase Membase Membase Cloudera’s Distribution for Apache Hadoop Cloudera’s Distribution for Apache Hadoop Cloudera’s Distribution for Apache Hadoop Cloudera’s Distribution for Apache Hadoop Cloudera’s Distribution for Apache Hadoop Cloudera’s Distribution for Apache Hadoop Cloudera’s Distribution for Apache Hadoop big data

58. real-time data collection, analysis

59. shared data platform

61. aggregation of mixed data sources

62. structured and un/semi-structured data

63. transform and loadCloudera’s Distribution for Apache Hadoop big data

64. Target markets big audience Enterprise applications event monitoring sensor data compliance and regulatory reporting intelligence analysis fraud detection Web applications social games SaaS e-commerce systems clickstream analysis ad and offer targeting systems Membase Cloudera’s Distribution for Apache Hadoop big data

65. Necessity BigTable and MapReduce – Google Dynamo – Amazon Hadoop, Pig, HBase – Yahoo Cassandra, Hive - Facebook Voldemort – Linkedin FlockDB – Twitter Hypertable – Zvents Neo4J – Windh Technologies Memcached – Danga Interactive MongoDB – Doubleclick Membase – Zynga

69. Thank You matthew.aslett@the451group.com

70. Accelerating Ad Targeting Decisions with Hadoop and Membase PeroSubasic, Aol pero.subasic@teamaol.com

71. Overview Online advertising overview Large-scale Analytics at Aol Current Architecture and Data Flows Hadoop+Membase current use cases RT Architecture Proposal RT Use Case: RT Contextual Segmentation of Users Conclusion

73. CPC = Cost Per Click, e.g. $2 per click

76. Predictive Segments

78. Use Cases Today data set enrichment: given a field in a data set stored on HDFS, enrich by adding related fields; media -> campaign -> advertiser chain blackboard for inter-process/job communication: contextual segmentation pipelines; predictive modeling can load per-campaign models to be used for large-scale scoring larger map-side joins (where HadoopDistributedCache and in-memory process/task cache is insufficient) aggregations with large number of item lookups, e.g. user-level contextual profiles aggregated from visited url contextual profies stored in memcache Flume integration for data flow reliability end recovery segment generation currently carried out through Hadoop pipelines and uploaded into server-side Membase for targeting but: strong tendency to move closer to ad serving motivates thinking about new architectures to reduce segment generation time

79. RT Framework: Capture, Compute and Forward Flume Ingestion Data Feeds CAPTURE COMPUTE FORWARD Big Data Loop Membase (back-end) Compute Cluster Membase (front-end) and ad-serving logic Hadoop

80. Features Ahead bucket lifecycle management (creation, sizing, deposition) asynchronous stream with all mutation operators iteration through key space without knowing the keys in advance (making key space Ord?) regex or range-based key iteration for finer-grain key space control bucket drain to HDFS event-based synchronization between instances (TAP)

81. RT Contextual Segmentation Flume Ingestion Data Feeds User-ContentIDMapper Membase Active Event Frame Membase + ad-serving logic User-Segment Mapper UC Map US Short-term Map ContentID-Segment Map Event-based updates Daily Map updates Hadoop US Long-term Map

83. Closing Remarks Exciting Times Need is real and recognized Technological capability is within reach Q/A Contact: pero.subasic@teamaol.com

85. PeroSubasic, Chief Architect, AOL

86. Frank Weigel, Director of Product Management, Couchbase (formerly Membase)