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How to Crunch Petabytes with Hadoop and Big Data using InfoSphere BigInsights and Streams

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How to Crunch Petabytes with Hadoop and Big Data using InfoSphere BigInsights and Streams

  1. 1. How to Crunch Petabytes with Hadoop and Big Data using InfoSphere BigInsights and Streams Tom Deutsch, IBMVladimir BVl di i Bacvanski, Founder, SciSpike ki F d S iS ikvladimir.bacvanski@scispike.comStephen Brodsky, Technical Executive and Distinguished Engineer, b d k @ ibAugust 24, 2011 © 2011 IBM Corporation & SciSpike
  2. 2. Who are we? Dr. Vladimir Bacvanski – Consultant, trainer, and mentor focusing on making clients successful in adopting new data and software approaches – Over 20 years of experience y p – Founder of SciSpike – a training and consulting firm specializing in advanced software and data technologies Stephen Brodsky, Ph.D. – Di ti Distinguished E i i h d Engineer and T h i l E d Technical Executive f IBM Bi D t ti for Big Data initiatives at the IBM Silicon Valley Laboratory – Previously led the architecture for the Optim Data Studio product line and pureQuery and was a member of the architecture team for DB2 pureXML, Rational Application Developer (RAD), and WebSphere. 2 © 2011 IBM Corporation & SciSpike
  3. 3. Agenda  The “Big Data challenge: smarter analytics for a Big Data” smarter planet  How to do it? – The big data challenge –FFoundations of Big D d i f Bi Data approaches h – MapReduce and Hadoop – Real-time data and stream processing – Integration with existing systems 3 © 2011 IBM Corporation & SciSpike
  4. 4. The “Big Data” ChallengeAugust 24, 2011 © 2011 IBM Corporation & SciSpike
  5. 5. The World is Changing and Becoming More… More INSTRUMENTED INTERCONNECTED INTELLIGENT The Th resulting explosion of information creates a need for lti l i fi f ti t df a new kind of intelligence …to help build a Smarter Planet 5 © 2011 IBM Corporation & SciSpike
  6. 6. Information is Growing at a Phenomenal Rate . . . . 44x 44 as much data and content over coming decade 80% Of world’s data is unstructured 2020 35 zettabytes (35 billion terabytes) 2009800,000 petabytes Source: IDC, The Digital Universe Decade – Are You Ready?, May 2010 6 © 2011 IBM Corporation & SciSpike
  7. 7. The BIG Data Challenge • Manage and benefit from massive and growing amounts of data • Handle varied data formats (structured, unstructured, semi-structured) and increased data velocity • Exploit BIG Data in a timely and cost effective fashion COLLECT MANAGE Collect Manage Integrate INTEGRATE Analyze ANALYZE7 © 2011 IBM Corporation & SciSpike
  8. 8. What clients are saying . . .  Lots of potentially valuable data is dormant or discarded p y due to size/performance considerations  Large volume of unstructured or semi-structured data is not worth semi structured integrating fully (e.g. Tweets, logs, . . .)  Not clear what should be analyzed (exploratory iterative) (exploratory,  Information distributed across multiple systems and/or Internet  Some information has a short useful lifespan  Volumes can be extremely high  Analysis needed in the context of existing information (not stand alone) 8 © 2011 IBM Corporation & SciSpike
  9. 9. Big Data Presents Big Opportunities Extract insight from a high volume, variety and velocity of data in a timely and cost-effective manner Variety: Manage and benefit from diverse data types and data structures Velocity: Analyze streaming data and large volumes of persistent data Volume: Scale from terabytes to zettabytes ettabytes9 9 © 2011 IBM Corporation & SciSpike
  10. 10. Streams and Oceans of Information . . . . Information oceans Information streams Information stored outsideHighHi h speed information flowing in di f ti fl i i conventional systems. Data may ti l t D t real-time, often transient originate from the Web or different Information from sensors, instruments, internal different systems etc. etc Information flowing from real-time logs  Collection of what has streamed and activity monitors  Information from social media, logs, click Streaming content like audio and video streams, emails, etc. High speed transactions like tickers, trades, or traffic systems  Unstructured or mixed schema documents like claims, forms, desktop applications, etc.  Structured data from disparate systems10 © 2011 IBM Corporation & SciSpike
  11. 11. Applications for Big Data Analytics Smarter Healthcare Multi-channel sales Finance Homeland security Traffic Control Telecom Manufacturing Trading Analytics Many more! 11 © 2011 IBM Corporation & SciSpike
  12. 12. Use Case Example: Energy Company Business scenario  Analyze large volumes of public and private weather data for alternative energy business  E i ti hi h Existing high-performance computing f ti hardware, limited staff Technical challenges  High data volume: 2+ PB  Range of q y types g query yp - Avg temp in given location? (Small result) - Geo pts where ice may form on wind turbines? (Large result derived values – result, icing determined by humidity + temp.)  Run on system with non-Hadoop apps12 © 2011 IBM Corporation & SciSpike
  13. 13. Use Case Example: Global Media Firm Business scenario  Identify unauthorized content streaming in digital media (piracy) - Quantify annual revenue loss - Analyze trends  Monitor social media sites to identify dissemination of pirated content. Time sensitive! Technical challenges  High variety of unstructured and semi- structured data. t t dd t  Initial focus: text analytics over 1 year’s worth of social media data. Look for live streaming URLs, sentiment, event info, etc.  Complex rules to qualify & classify info  Future potential for video analysis13 © 2011 IBM Corporation & SciSpike
  14. 14. IBM WatsonIBM Watson is a breakthrough in analytic innovation, but it is only successful because of the quality of the information from which it is working. 14 © 2011 IBM Corporation & SciSpike
  15. 15. Big Data and Watson Big Data technology is used to build Watson technology offers great potential Watson’s knowledge base for advanced business analytics Watson uses the Apache Hadoop open framework to distribute the workload for loading information into memory. CRM Data POS Data Social Media Approx. 200M pages of text (To compete on Jeopardy!) Distilled Insight - Spending habits - Social relationships - Buying trends InfoSphere BigInsights oSp e e g s g ts Watson’s Memory Advanced search and analysis 15 © 2011 IBM Corporation & SciSpike
  16. 16. Customer EngagementsUse patterns Common requirements• Customer sentiment analysis (cross- (cross • Extract business insight from large volumes of sell, up-sell, campaign management) raw data (often outside operational systems)• Integrated retail and web customer • Integrate with other existing software behavior modeling g • Ready for enterprise use• Predictive modeling (credit card fraud)• System log analytics (reduce operational risk) p ) Consumer Text, Blog, Text Blog Weblog Insight Click streams Multi-channel sales Log & transactions Next Gen Text Analytics Biological Sequences Fraud Models Operational system & streams data sources p y New Business Stat st ca ode Statistical Model Development Building1616 © 2011 IBM Corporation & SciSpike
  17. 17. The approach to crunching big dataAugust 24, 2011 © 2011 IBM Corporation & SciSpike
  18. 18. How to approach Big Data analytics? InfoSphere BigInsights and InfoSphere Streams • Analytics for data in-motion and at-rest • Platform for processing large volumes of diverse data • Complements and integrates with existing software solutions 18 © 2011 IBM Corporation & SciSpike
  19. 19. Addressing the Key Requirements 1. Platform for V3 – Variety, Velocity, Volume  Variety - manage data & content “As Is”  Handle any velocity - low-latency streams and large volume batch  Volume - huge volumes of at-rest or streaming data Big Data Platform 2 Analytics for V3 2.  Analyze Sources in their native format - text, data, rich content  Analyze all of the data - not just a subset  Dynamic analytics - automatic adjustments and actions 3. Ease of Use for Developers and Users  Developer UIs, common languages & automatic optimization  End-user UIs & visualization 4. Enterprise Class  Failure tolerance, Security and Privacy  Scale Economically 5. Extensive Integration Capabilities  Integrate wide variety of sources  Leverage enterprise integration technologies 19 © 2011 IBM Corporation & SciSpike
  20. 20. Big D t I iti tiBi Data InitiativeVolumes of diverse persistent data diverse, Analytic applications for “Big Data” InfoSphere p BigInsights Warehouse Traditional warehouse applications IBM Confidential InfoSphere StreamsReal-time streaming data 20 © 2011 IBM Corporation & SciSpike
  21. 21. BigInsights Summary  BigInsights = analytical platform for persistent “Big Data” – Based on open source & IBM technologies  Distinguishing characteristics – Built-in analytics . . . . Enhances business knowledge – Enterprise soft are integration . . . . Complements and e tends software extends existing capabilities – Production-ready platform . . . . Speeds time-to-value; simplifies development and maintenance 21 © 2011 IBM Corporation & SciSpike
  22. 22. Big Data Platform Vision Bringing Big Data to the Enterprise Data Big Data Solutions Warehouse Information Integration Big Data User Environments Developers End Users Administrators Master Data Mgmt IN NTEGRATIOAGENTSA Database Big Data Enterprise Engines Content ON Analytics Business Analytics Streaming Analytics g y Internet Scale Analytics y Marketing Open Source Foundational Components Data Growth Management 22 © 2011 IBM Corporation & SciSpike
  23. 23. InfoSphere BigInsights v 1.1 Platform for volume, variety, velocity -- V3  Hadoop foundation Analytics for V3  Text analytics & tooling Enterprise Edition Licensed Usability Web admin console, LDAP authentication  Web administrative lass RDBMS, warehouse connectivity nterprise cl console Text analytics Basic Edition  Integrated install Spreadsheet-style analytic tool Free download Flexible job scheduler  Spreadsheet-style analytic t l l ti tool Apache 24 x 7 Web En Hadoop support Enterprise Class  Storage, security, cluster management Breadth of capabilities Integration  Connectivity to DB2, Netezza 23 © 2011 IBM Corporation & SciSpike
  24. 24. BigInsights Platform: Key Ideas  Flexible, enterprise-class support for processing large volumes of data – Based on Google’s MapReduce technology – Inspired by Apache Hadoop; compatible with its ecosystem a d sp ed pac e adoop; co pat b e t ts ecosyste and distribution – Well-suited to batch-oriented, read-intensive applications – Supports wide variety of data  Enables applications to work with thousands of nodes and petabytes of data in a highly parallel, cost effective manner t b t fd t i hi hl ll l t ff ti – CPU + disks = “node” – Nodes can be combined into clusters – New nodes can be added as needed without changing • Data formats • How data is loaded • How jobs are written 24 © 2011 IBM Corporation & SciSpike
  25. 25. The M R dTh MapReduce Programming Model P i M d l "Map" step: Map – Input split into pieces – W k nodes process individual pieces i parallel ( d Worker d i di id l i in ll l (under global control of the Job Tracker node) – Each worker node stores its result in its local file system where a reducer is able to access it "Reduce" step: – Data is aggregated (‘reduced” from the map steps) by ( reduced worker nodes (under control of the Job Tracker) – M lti l reduce tasks can parallelize th aggregation Multiple d t k ll li the ti 25 25 © 2011 IBM Corporation & SciSpike
  26. 26. What is Hadoop? Apache Hadoop = free, open source framework for data- intensive applications – Inspired by Google technologies (MapReduce, GFS) – Well-suited to batc o e ted, read-intensive app cat o s e su ted batch-oriented, ead te s e applications – Originally built to address scalability problems of Nutch, an open source Web search technology Enables applications to work with thousands of nodes and petabytes of data in a highly parallel, cost effective manner – CPU + disks of commodity b = H d di k f dit box Hadoop “ d ” “node” – Boxes can be combined into clusters – New nodes can be added as needed without changing • Data formats • How data is loaded • How jobs are written 26 © 2011 IBM Corporation & SciSpike
  27. 27. Two Key Aspects of Hadoop  MapReduce framework – How Hadoop understands and assigns work to the nodes (machines)  Hadoop Distributed File System = HDFS – Where Hadoop stores data – A file system that spans all the nodes in a Hadoop cluster – It links together the file systems on many local nodes to make them into one big file system 27 © 2011 IBM Corporation & SciSpike
  28. 28. Logical MapReduce Example: Word Count Content of Input Documents Hello World Bye World map(String key, String value): Hello IBM // key: document name // value: document contents Map 1 emits: < Hello, 1> for each word w in value: < World, 1> EmitIntermediate(w, 1 ); EmitIntermediate(w "1"); < Bye, 1> Bye < World, 1> reduce(String key, Iterator values): ( g y, ) Map 2 emits: < Hello, 1> // key: a word < IBM, 1> // values: a list of counts Reduce (final output): int result = 0; < Bye, 1> for each v in values: < IBM, 1> result += ParseInt(v); < H ll 2> Hello, 2 Emit(AsString(result)); < World, 2> 28 © 2011 IBM Corporation & SciSpike
  29. 29. How To Create MapReduce Jobs MapReduce development in Java p p – Low level, very flexible – Time consuming development Hive – Open source language / Apache sub-project sub project – Provides a SQL-like interface to Hadoop Pig – Data flow language / Apache sub-project Jaql – A query language for JSON – Useful for loosely structured data 29 © 2011 IBM Corporation & SciSpike
  30. 30. Management Tools: Web Console  Graphically manage cluster, jobs, HDFS  Sample administration tasks – Start/Stop Servers – Add/Remove Servers – Server Status Details (Log) 30 © 2011 IBM Corporation & SciSpike
  31. 31. Spreadsheet likeSpreadsheet-like Analysis Tool Web-based analysis BigSheets and visualization tool Spreadsheet-like interface – Define and manage long running data collection j b ll i jobs – Analyze content of the text on the pages that have been retrieved 31 © 2011 IBM Corporation & SciSpike
  32. 32. Text Analytics • Distill structured info from unstructured data "Acquisition" • Sentiment analysis "Address" Address "Alliance" • Consumer behavior "AnalystEarningsEstimate" • Illegal or suspicious activities "City" "CompanyEarningsAnnouncement" CompanyEarningsAnnouncement • ... "CompanyEarningsGuidance" "Continent" "Country" • Pre-built library of text annotators for common "County" County business entities "DateTime" "EmailAddress" "JointVenture" • Rich language and tooling to build custom g g g "Location" Location annotators "Merger" "NotesEmailAddress" "Organization" • Support for Western languages ( g , pp g g (English, "Person" Person Dutch/Flemish, French, German, Italian, "PhoneNumber" Portuguese, or Spanish) plus select Asian "StateOrProvince" languages (Japanese, Chinese) "URL" "ZipCode" ZipCode 32 32 © 2011 IBM Corporation & SciSpike
  33. 33. Eclipse basedEclipse-based Text Analytics Development 33 © 2011 IBM Corporation & SciSpike
  34. 34. So What Does This Result In?  Easy To Scale  Fault Tolerant and Self-Healing  Data Agnostic  Extremely Flexible 34 © 2011 IBM Corporation & SciSpike
  35. 35. Working with streaming data: a new paradigm  Conventional processing: static data Queries Data Results  Real-time processing: streaming data Data Queries Results 35 © 2011 IBM Corporation & SciSpike
  36. 36. Real-TimeReal Time Data with InfoSphere Streams Source Sink Streaming analytic applications Adapters Operator Repository Adapters – M lti l i Multiple input streams t t – Advanced streaming analytics Eclipse based IDE InfoSphere Streams Studio – Define sources, apply operators, (IDE for Streams Processing Language) define intermediary and final output sinks – User defined operators in Java or C++ Automated, Automated Optimized DeployO i i i Optimizing compiler automates il and Management (Scheduler) deployment and connections – Extremely low latency y y – Cluster of up to 125 nodes 36 © 2011 IBM Corporation & SciSpike
  37. 37. Scalable stream processing  InfoSphere Streams provides – A programming model and IDE f d fi i d t sources and i d l d for defining data d software analytic modules called operators that are fused into process execution units (PEs) – infrastructure to support the composition of scalable stream processing applications from these components – deployment and operation of these applications across distributed p y p pp x86 processing nodes, when scaled processing is required – stream connectivity between data sources and PEs of a stream processing application37 © 2011 IBM Corporation & SciSpike
  38. 38. Merging the Traditional and Big Data Approaches Traditional Approach Big Data Approach Structured & Repeatable Analysis Iterative & Exploratory Analysis IT Business Users Delivers a platform to Determine what enable creative bl ti question to ask discovery IT Business Structures the Explores what data to answer questions could be that question q asked Monthly sales reports Brand sentiment Profitability analysis Product strategy Customer surveys Maximum asset utilization 38 © 2011 IBM Corporation & SciSpike
  39. 39. BigInsights and the data warehouse: filtering andsummarizing “Big Data” BigInsights • Broader analytic coverage • Exploits IT investments while p Data warehouse minimizing burden 39 © 2011 IBM Corporation & SciSpike
  40. 40. BigInsights as a “queryable archive for growing queryable archive”data warehouses BigInsightsData warehouse • Offl d “cold” or dated warehouse info but Offload “ ld” d t d h i f b t maintain access for further exploration • Keep warehouse size manageable and focused on well-known business analytic needs 40 © 2011 IBM Corporation & SciSpike
  41. 41. Trends and directions  Enterprise software integration – Data warehouses, RDBMSs – ETL platforms l tf – Business intelligence tools – Applications – ...  Diverse range of analytics – Text – Image / video (e.g., content based user profiling) (e g content-based – Predictive modeling (e.g., ranking and classification based on machine learning) – ...  Sophisticated, scalable infrastructure for processing massive data volumes – High-performance file system with full POSIX compliance, g g p y p , granular security – Fully recoverable and restartable workflows – Parallel, distributed indexing for text (“BigIndex”) – Read-optimized column store p – Tooling for administrators, programmers, analysts – ... 41 © 2011 IBM Corporation & SciSpike
  42. 42. Integrating Relational, Streams, and BigInsights Traditional /Traditional RelationalWarehouse Data Sources Database & At-rest Results Warehouse data analytics Non-Traditional / Streams Non-Relational N R l ti l Data Sources In-Motion Ultra Low Analytics Latencyy Results Varied data InfoSphere Big Insights formats Massive Scale Big Data Semi-structured, Batch oriented Results unstructured... data analytics 42 © 2011 IBM Corporation & SciSpike
  43. 43. Typical Strategy for Analytics ETL SQL Analytics, Mining Data warehouse / martsSourceSourcesS Transform/ Extract Load subset43 © 2011 IBM Corporation & SciSpike
  44. 44. Emerging requirements for analytics SQL Analytics, Mining ETL, ELT (MR BI, Mining) SourceStructured Transform, Analyze Warehouses / marts Sources Transform/ Extract subset Load BigInsights g g Source Repository Other Sources Explore large volumes of “raw” or diverse data. Discover, analyze new insights with BigInsights 44 © 2011 IBM Corporation & SciSpike
  45. 45. Conclusions – Scale out to crunch petabytes – We need a mix of technologies • Data at rest: MapReduce, Hadoop and beyond • Data in motion: stream processing – To be successful, integrate with conventional technologies 45 © 2011 IBM Corporation & SciSpike
  46. 46. Getting in touch  Stephen Brodsky – IBM – Email:  InfoSphere BigInsights – ttp // 0 b co /so t a e/data/ osp e e/b g s g ts/  InfoSphere Streams –  Vladimir Bacvanski - SciSpike – Email: – Blog: – Twitter: – LinkedIn: p 46 © 2011 IBM Corporation & SciSpike

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