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Ibm big data hadoop summit 2012 james kobielus final 6-13-12(1)


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Ibm big data hadoop summit 2012 james kobielus final 6-13-12(1)

  1. 1. June 2012 IBM Big Data The Marriage of Hadoop and Data Warehousing James Kobielus Senior Program Director, Product Marketing, Big Data, IBM © 2012 IBM Corporation
  2. 2. Hadoop and DW are fast being joined into a new platform paradigm: the Hadoop DW 2 © 2012 IBM Corporation
  3. 3. Agenda  Big Data: “3 Vs” and myriad use cases  Big Data: diverse workloads  Big Data: emergence of the “Hadoop DW” 3 © 2012 IBM Corporation
  4. 4. Agenda  Big Data: “3 Vs” and myriad use cases  Big Data: diverse workloads  Big Data: emergence of the “Hadoop DW” 4 © 2012 IBM Corporation
  5. 5. Scalability Imperative: 3 “Vs” Drive Big Data Everywhere Information Radical Extreme from Everywhere Flexibility Scalability Volume Velocity Variety 5 12 terabytes of Tweets created daily 5 million trade events per second 100’s from surveillance cameras video feeds © 2012 IBM Corporation
  6. 6. More Business Use Cases for Big Data Across Enterprise 6 © 2012 IBM Corporation
  7. 7. More Mission-Critical Apps Ride on Big Data Platforms Advanced Analytic Applications  Integrate and manage the full variety, velocity and volume of data  Apply advanced analytics to information in its native form Big Data Platform  Visualize all available data for ad-hoc analysis Process and analyze any type of data and discovery Accelerators  Development environment for building new analytic applications  Integration and deploy applications with enterprise grade availability, manageability, security, and performance • Analyze data in motion • Visualization and • MapReduce / noSQL exploration • Machine Learning • Scalability • Text Analytics • Hardware • Text Search acceleration • Data Discovery • Stream computing 7 © 2012 IBM Corporation
  8. 8. Big Data: Business Crucible for Practical Data Science Business and IT Identify Information Sources Available New insights IT Delivers a drive integration Platform that to traditional enables creative technology exploration of all available data and content Business determines what questions to ask by exploring the data and relationships 8 © 2012 IBM Corporation
  9. 9. Big Data Initiatives: Fueled by Practical Data Science Analyze a Variety of Information Novel analytics on a broad set of mixed information that could not be analyzed before Analyze Information in Motion Streaming data analysis Large volume data bursts and ad-hoc analysis Analyze Extreme Volumes of Information Cost-efficiently process and analyze PBs of information Manage & analyze high volumes of structured, relational data Discover and Experiment Ad-hoc analytics, data discovery and experimentation Manage and Plan Enforce data structure, integrity and control to 9 ensure consistency for repeatable queries IBM Corporation © 2012
  10. 10. Big Data: Marriage of Established & Emerging Approaches Established Approach Emerging Approaches Structured, analytical, logical Creative, holistic thought, intuition DW Hadoop, etc. Transaction Data Web Logs Internal App Data Social Data Structured Unstructured Structured Enterprise Exploratory Repeatable Exploratory Mainframe Data Repeatable Linear Integration Iterative Iterative Text Data: emails Linear Monthly sales reports Brand sentiment Profitability analysis Product strategy OLTP System Data Sensor data: images Customer surveys Maximum asset utilization ERP data Traditional New RFID Sources Sources 10 © 2012 IBM Corporation
  11. 11. Agenda  Big Data: “3 Vs” and myriad use cases  Big Data: diverse workloads  Big Data: emergence of the “Hadoop DW” 11 © 2012 IBM Corporation
  12. 12. Continuous Social Media Monitoring and Analytics Data Set Information extracted • 1.1B tweets • Buzz and sentiment • 5.7M blog and forum posts • Gender, Location and Occupation • 3.5M relevant messages • Fans • 97K referencing Product A • Intent to in purchase • 18K referencing Product B • Specific attributes of products 12 © 2012 IBM Corporation
  13. 13. Content mining, natural language processing, & classification  How it works Unstructured text (document, email, etc) – Parses text and detects meaning with extractors Football World Cup 2010, one team – Understands the context in which the text is distinguished themselves well, losing to analyzed the eventual champions 1-0 in the Final. – Hundreds of pre-built extractors for names, addresses, phone numbers, organizations, Early in the second half, Netherlands’ URL, Datetime, etc. striker, Arjen Robben, had a breakaway, but the keeper for Spain, Iker Casillas  Accuracy made the save. Winger Andres Iniesta – Highly accurate in deriving meaning from scored for Spain for the win. complex text  Performance – AQL language optimized for MapReduce Classification and Insight World Cup 2010 Highlights 13 © 2012 IBM Corporation
  14. 14. Entity Extraction and Integration 14 © 2012 IBM Corporation
  15. 15. Statistical Analysis, Predictive Modeling, & Machine Learning Enables Machine learning (ML) on massive datasets  R and Matlab-like syntax for smooth adoption  Optimizations to generate low-level executions plans  Out-of-box and write-your-own analytic algorithms, e.g. Regression, Clustering, Classification, Pattern Mining, Ranking, etc.  Scale to massively parallel clusters from 10s to 1000s of machines and from Terabytes to Petabytes What are people talking about in social media about a product? 15 15 © 2012 IBM Corporation
  16. 16. Targeted E-Commerce and Next Best Action 16 © 2012 IBM Corporation
  17. 17. Predictive Complex Event Processing 17 © 2012 IBM Corporation
  18. 18. Intent and Sentiment Analysis Online flow: Data-in-motion analysis Data Sources Stream Computing and Analytics Timely Decisions Entity Predictive Data Ingest Text Analytics: Analytics: Analytics: and Prep Timely Insights Profile Action Resolution Determination Dashboard Hadoop System and Analytics Comprehensive Entity Social Media and Social Media Predictive Customer Text Analytics Analytics and Enterprise Data Customer Analytics Models Integration Profiles Offline flow: Data-at-rest analysis Reports 18 © 2012 IBM Corporation
  19. 19. Agenda  Big Data: “3 Vs” and myriad use cases  Big Data: diverse workloads  Big Data: emergence of the “Hadoop DW” 19 © 2012 IBM Corporation
  20. 20. Big Data: DW & Hadoop are Married in Spirit Cloud-facing architectures models Massively policies metadata aggregates parallel DQ MDM hubs marts processing cubes ETL databases DW views storage In-database memory staging production cache in-database analytics nodes tables analytics operational data stores Mixed workload management Hybrid storage layers 20 © 2012 IBM Corporation
  21. 21. Hadoop is Core of Next-Gen Big Data DW  Vendor-agnostic framework for massively parallel processing of advanced analytics against polystructured information  Leverages extensible framework for building advanced analytics and data management functions  Evolving rapidly in new directions  Being commercialized and adopted rapidly in enterprises  Vibrant open-source community and industry 21 © 2012 IBM Corporation
  22. 22. Hadoop, DW, and other Databases Co-Exist in Big Data Ecosystem Hadoop & In-memory NoSQL DW RDBMS Columnar OLAP Big Data staging, ETL, and Big Data SVOT and Big Data access preprocessing tier governance tier and interaction tier 22 © 2012 IBM Corporation
  23. 23. How Hadoop and DW Complement Each Other 23 © 2012 IBM Corporation
  24. 24. Single Version of Big Data: Where Hadoop DW Will Excel Monetizable intent to see a Monetizable intent to buy Kinda feel like going to movies tonight… Any I need a new digital camera for my food pictures, and recommendations? @Texas Angelika Texas recommendations around 300? I don’t think anyone understands how much I like What should I buy?? A mini laptop with Windows 7 OR a Apply watching movies. My 3rd trip to the threatre in 3 days. MacBook!??! Life Events Location announcements College: Off to Standard for my MBA! Bbye chicago! I’m at Starbucks Parque Tezontle Looks like we’ll be moving to New Orleans sooner than I thought. 24 © 2012 IBM Corporation
  25. 25. Hadoop DW Integration: What to Look For models  Hadoop distro functional depth policies metadata aggregates  EDW HDFS connector DQ MDM hubs marts cubes  Software, appliance, and cloud form factors for ETL databases Hadoop offerings storage staging nodes DW production views memory cache in-database  Pluggable storage layer for Hadoop offerings tables operational analytics  Bundled data management and analytics data stores offerings integrated with Hadoop solutions  Modeling, management, acceleration, and optimization tools  Real-time/low-latency capabilities integrated into Hadoop offerings  Robust availability, security, and workload management tools integrated with Hadoop offerings  And many more, focused on EDW-grade robustness, scalability, and flexibility! 25 © 2012 IBM Corporation
  26. 26. Consider Big Data Platform Accelerators Telecommunications Retail Customer CDR streaming analytics Intelligence Deep Network Analytics Customer Behavior and Lifetime Value Analysis Finance Social Media Analytics Streaming options trading Sentiment Analytics, Intent to Insurance and banking DW purchase models Public transportation Data mining Real-time monitoring and Streaming statistical analysis routing optimization Over 100 sample User Defined Standard Toolkits Industry Data Models applications Toolkits Banking, Insurance, Telco, Healthcare, Retail 26 © 2012 IBM Corporation
  27. 27. How Will You Do MDM on Your Hadoop DW? (A1) Unstructured Entity Integration (on BigInsights) – Complex analytics to populate master data set – Text Analytics: Rule language (AQL) for extracting entities, events, relationships from text and html documents MDM DaaS – Entity Integration: Rule language (HIL) to express & Applications customize the integration, cleansing, and aggregation of and Views the master entities (A2) Entity Repository (on MDM) – BigInsights Bridge: Generation of the MDM model for select cik, Officers, Directors public master entities, from the BigInsights model; and from Company bulk-loading of master entities Data services where name = ‘Citigroup’ – Query-based Application Development: Supports the generation of custom queries for individual applications Tooling based Queries on entity model A2 External data subscriptions (e.g., Acxiom) A1 Relational tables SELECT * FROM with master (SELECT t2.CIK as CIK, t2.NAME as NAME, t2.IS_FORMER_OFFICER as IS_FORMER_OFFICER, t2.IS_IMPORTANT_OFFICER as IS_IMPORTANT_OFFICER, t2.POSITION_NAME as POSITION_NAME, Text Analytics entities FROM tp.EARLIEST_DATE as EARLIEST_DATE, tp.IS_EARLIEST_EXACT as IS_EARLIEST_EXACT, tp.LATEST_DATE as LATEST_DATE, tp.IS_LATEST_EXACT as IS_LATEST_EXACT External public data and (SELECT t1.CIK as CIK, t1.NAME as NAME,t1.IS_FORMER_OFFICER as IS_FORMER_OFFICER, t1.IS_IMPORTANT_OFFICER as IS_IMPORTANT_OFFICER, p.NAME as POSITION_NAME, p.POSITIONSPK_ID as POSITIONSPK_ID sources Entity Integration FROM (SELECT o.CIK as CIK, o.NAME as NAME, o.IS_FORMER_OFFICER as IS_FORMER_OFFICER, o.IS_IMPORTANT_OFFICER as IS_IMPORTANT_OFFICER, o.OFFICERSPK_ID as OFFICERSPK_ID FROM DB2ADMIN.OFFICERS o (e.g., SEC/FDIC, WHERE o.OFFICER_OF = 567830643756635868 ) as t1 Twitter, Blogs, BigInsights InfoSphere MDM left outer join DB2ADMIN.POSITIONS p on t1.OFFICERSPK_ID= p.POSITIONOF ) as t2 Facebook) left outer join D2ADMIN.RANGEOFKNOWNDATES tp with Extensions UNION on t2.POSITIONSPK_ID = tp.RANGE_OF_KNOWN_DATES_FOR_POS ) // ( OUTER UNION) … 27 © 2012 IBM Corporation
  28. 28. IBM Big Data Platform New analytic applications drive the Analytic Applications requirements for a big data platform BI / Exploration / Functional Industry Predictive Content Reporting Visualization App App BI / Analytics Analytics Reporting • Integrate and manage the full IBM Big Data Platform variety, velocity and volume of data Visualization Application Systems • Apply advanced analytics to & Discovery Development Management information in its native form • Visualize all available data for ad- Accelerators hoc analysis • Development environment for Hadoop Stream Data System Computing Warehouse building new analytic applications • Workload optimization and scheduling • Security and Governance Information Integration & Governance © 2012 IBM Corporation
  29. 29. Thank You! 29 © 2012 IBM Corporation

Editor's Notes

  • Now if you recall, I talked about the EDW not going away and the Big Data system working with it. Just a couple of slides ago I talked about the IBM Big Data platform and I included commentary about IBM Information Server for integration and that’s what this slide is showing here. We know that we are now faced with two complementary analytical approaches – we have this traditional approach, we have this new approach – and when we bring these together, we need some help to figure out a way to get from the left sphere to the right sphere and that’s going to be enterprise integration. So IBM provides that; for example IIS has readers for HDFS and natively within DB2 is a UDF that can call a MapReduce program, and more. If you look at this slide, you can see that if you live in the SQL world, you can talk to the Big Data world, and vice versa.
  • Key Points IBM research developed a sophisticated text analytics engine – similar technology to what was demonstrated in Watson Its purpose is to identify meaning within text We have pre-built 100s of rules (annotators) that understand textual meaning – names (e.g., what is a first name v a last name), addresses (what is a street, apartment) among others. The annotators are context sensitive and discover the relationship between terms even if they are separate by text – for example, it discovers that Iker Casillas is a “keeper” even though the phrase “for Spain” is in between them Accuracy – our text analytics engine is very accurate and we’ve done testing that indicates it is 2-3x more accurate than some alternatives It is also highly performant – it is designed for use in Big Data and map reduce parallel processing
  • Confidential IBM/Expedia 9/13/11 Confidential IBM/Expedia 9/13/11
  • Key Points - Integrate v3 – the point is to have one platform to manage all of the data – there’s no point in having separate silos of data, each creating separate silos of insight. From the customer POV (a solution POV) big data has to be bigger than just one technology Analyze v3 – very important point – we see big data as a viable place to analyze and store data. New technology is not just a pre-processor to get data into a structured DW for analysis. Significant area of value add by IBM – and the game has changed – unlike DBs/SQL, the market is asking who gets the better answer and therefore sophistication and accuracy of the analytics matters Visualization – need to bring big data to the users – spreadsheet metaphor is the key to doing son Development – need sophisticated development tools for the engines and across them to enable the market to develop analytic applications Workload optimization – improvements upon open source for efficient processing and storage Security and Governance – many are rushing into big data like the wild west. But there is sensitive data that needs to be protected, retention policies need to be determined – all of the maturity of governance for the structured world can benefit the big data world