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Gilbane Boston 2011 big data

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"Get Ready for Big Data" presentation from Gilbane Boston 2011; for more details, see http://gilbaneboston.com/conference_program.html#t2 and http://pbokelly.blogspot.com/2011/12/gilbane-boston-2011-big-data.html

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Gilbane Boston 2011 big data

  1. 1. Get Ready for Big Data Wednesday November 30, 2011 2:40 – 4:00Peter OKelly Principal Analyst, OKelly AssociatesHadley Reynolds Managing Director, Next Era ResearchKathleen Reidy Senior Analyst, 451 Research
  2. 2. Agenda• Big data in context• Big structured data• Big unstructured data• Big opportunities and risks• Q&A 2
  3. 3. Big Data in Context• What is “big data”? – Unhelpfully, both “big data” and “NoSQL,” generally considered a key part of the big data wave, are defined more in terms of what they’re not than what they are – A typical big data definition (Wikipedia): • “*…+ datasets that grow so large that they become awkward to work with using on-hand database management tools” 3
  4. 4. Big Data in Context• With thanks to the Business SOA blog: – “*…+ describe Big Data in the same way that the Hitchhikers Guide to the Galaxy described space: – ‘Space,’ it says, ‘is big. Really big. You just wont believe how vastly, hugely, mindbogglingly big it is. I mean, you may think its a long way down the road to the chemists, but thats just peanuts to space, listen...’” 4
  5. 5. Big Data in Context• Why is big data a big deal now? – Commodity hardware and the Internet • Capability and price/performance curves that continue to defy all economic “laws” • Also facilitating compelling cloud services – Maturation and uptake of open source software, e.g., Hadoop • Powerful and often no- or low-cost – IT market • Enthusiasm for “NoSQL” systems • Frustration with incumbent information management vendors – Useful new data sources/resources, e.g., social network activity graphs, the “Internet of things,” sensor networks… – Competitive and compliance imperatives 5
  6. 6. Big Data in Context• A big data reality check – “Mindbogglingly”-scale information management is not new • Consider, e.g., VLDB, multi-billion document repositories, and the World Wide Web… – What is new and compelling • The combination of market dynamics producing new capability and price/performance curves • Cloud – No deep capital investment required to get started – Cloud-based information resources • Some innovative marketing, suggesting – Self-proclaimed next-generation big data systems are magical and revolutionary – Deployed systems are obsolete and wasteful 6
  7. 7. A Big-Picture Framework• A digital information item dichotomy – Resources (~unstructured information) • Digital artifacts optimized to convey stories – Organized in terms of narrative, hierarchy, and sequence • Examples: books, magazines, documents (e.g., PDF, Word), Web pages, XBRL documents, video, hypertext… – Relations (~structured information) • Application-independent descriptions of real-world things and relationships • Examples: business domain databases, e.g., customer, sales, HR… 7
  8. 8. A Big-Picture Framework Resource Relation 8
  9. 9. A Big-Picture Framework Resources RelationsConceptual Resources and links Entities, attributes, relationships, and identifiersLogical Model: hypertext Model: extended relational Language: XQuery (ideally) Language: SQLPhysical Indexing (e.g., scalar data types, XML, full-text), locking and isolation levels, federation, replication, in-memory databases, columnar storage, table spaces, caching, and more 9
  10. 10. Agenda• Big data in context• Big structured data• Big unstructured data• Big opportunities and risks• Q&A 10
  11. 11. Big Structured Data• NoSQL• Hadoop• RDBMS reconsidered• Back to the bigger picture 11
  12. 12. NoSQL• No clear consensus on what “NoSQL” means – Started with what it’s against, not what it’s about • And often finds a receptive audience due to frustration with RDBMS business-as-usual – The “NoSQL” meme is a moving target • Initially implied “Just say ‘no’ to SQL” • Later quietly redefined as “Not Only SQL” • What may be next: “New Opportunities for SQL” – I.e., some developers may reconsider the value of SQL and RDBMSs, after hitting NoSQL limitations 12
  13. 13. A NoSQL Taxonomy• From the NoSQL Wikipedia article: 13
  14. 14. NoSQL Perspectives• The “NoSQL” meme confusingly conflates – Document database requirements • Best served by XML DBMS (XDBMS) – Physical model decisions on which only DBAs and systems architects should focus • And which are more complementary than competitive with RDBMS/XDBMS – Object databases, which have floundered for decades • But with which some application developers are nonetheless enamored, for minimized “impedance mismatch,” despite significant information management compromises – Semantic models • Also more complementary than competitive with RDBMS/XDBMS 14
  15. 15. Hadoop• Hadoop is often considered central to big data – Originating with Google’s MapReduce architecture, Apache Hadoop is an open source architecture for distributed processing on networks of commodity hardware• Commercial application domains include (from Wikipedia) – Log and/or clickstream analysis of various kinds – Marketing analytics – Machine learning and/or sophisticated data mining – Image processing – Processing of XML messages – Web crawling and/or text processing – General archiving, including of relational/tabular data, e.g. for compliance 15
  16. 16. Hadoop• Hadoop is popular and rapidly evolving – Most leading information management vendors, including Microsoft, have embraced Hadoop – There is now a Hadoop ecosystem 16
  17. 17. RDBMS Reconsidered• RDBMS incumbents appear to be under siege, with – IT frustration with RDBMS business-as-usual • Counterproductive RDBMS vendor policies and attitudes • DBA modus operandi often seen as excessively conservative – Conventional wisdom about RDBMS limitations for, e.g., • “Web scale” • “Agility” • The application/database “impedance mismatch” – The advent of open source and/or specialized DBMSs • E.g., MySQL is the M in the “LAMP stack” • “The end of the one-size-fits-all DBMS era” 17
  18. 18. RDBMS Reconsidered• An RDBMS reality check – Leading RDBMS products and open source initiatives are very powerful and flexible • And will continue to evolve, e.g., with the mainstream deployment of massive-memory servers and solid state disk (SSD) storage – And they continue to expand • E.g., in-database processing, with, for example, analytics engines running within DBMS kernels – But the RDBMS incumbents nonetheless face unprecedented challenges • Which sometimes resonate with frustrated architects and developers because of negative experiences that have more to do with how RDBMSs were used rather than what RDBMSs can effectively address 18
  19. 19. RDBMS in the Big-Picture Framework Resources RelationsConceptual Resources and links Entities, attributes, relationships, and identifiersLogical Model: hypertext Model: extended relational Language: XQuery Language: SQLPhysical Indexing (e.g., scalar data types, XML, full-text), locking and isolation levels, federation, replication, in-memory databases, columnar storage, table spaces, caching, and more 19
  20. 20. RDBMS Reconsidered• A Forrester big data reality check (from “Stay Alert To Database Technology Innovation,” 11/19/2010): – “For 90% of BI use cases, which are often less than 50 terabytes in size, relational databases still are good enough” (p. 4) – “Traditional relational databases are still good enough for the majority of transactional use cases” (p. 5) 20
  21. 21. Back to the Bigger Picture• Compared with traditional enterprise data management, big data is – Essentially a collection of specialized physical models for very large, analysis-oriented data management – Expanding to encompass resources as well as relations – More about the potential for displacing expensive and closed/proprietary distributed processing alternatives than displacing RDBMS or XDBMS 21
  22. 22. Structured Big Data: Recap• Substantive, sustainable, and synergistic – RDBMS – XDBMS – Hadoop – The cloud as an information management platform• Vaguely defined, transitory, and over-hyped – NoSQL 22
  23. 23. Agenda• Big data in context• Big structured data• Big unstructured data• Big opportunities and risks• Q&A 23
  24. 24. Big Unstructured Data• Finding Facts about Data – IDC/EMC• Patterns for Unstructured Big Data• How-to issues – who will know? 24
  25. 25. http://www.emc.com/leadership/programs/digital-universe.htm 25
  26. 26. 26
  27. 27. 27
  28. 28. 4/28/2011 28
  29. 29. 29
  30. 30. 30
  31. 31. 4/28/2011 31
  32. 32. 32
  33. 33. 33
  34. 34. Facebook:800M users500M visitors/day 34$100B potential value @ IPO
  35. 35. http://inmaps.linkedinlabs.com/ 35
  36. 36. Unstructured Big Data Patterns• Search• Social• Mobile• Online Activities/Digital Marketing• Inquiry/Detection – Connecting Dots• Question Answering 36
  37. 37. Mobile Adds:Location data pointsVoice searchesSiri questionsApp history profileBrowse history profileSearch history profilePast purchase profileCamera-generated outputs/inputsCoupon delivery & merchandisingFriends locationsSocial searchLocal ad-match algo opportunities 37
  38. 38. 4/28/2011 38
  39. 39. Online Activities/Digital Marketing 39
  40. 40. • Inquiry/Detection – Connecting Dots – Intelligence – Law Enforcement – Fraud Detection (Government, Financial, Health, …) – eDiscovery 40
  41. 41. Social Media Monitoring 41
  42. 42. Question Answering 4/28/2011 42
  43. 43. Question Answering Beyond Jeopardy 43
  44. 44. Twitter Analytics Questions• What can we tell about a user from their tweets? – from the tweets of those they follow? – from the tweets of their followers? – from the ratio of followers/following• What graph structures lead to successful networks?• User reputation?• Sentiment analysis?• What features get a tweet retweeted? – How deep is the retweet tree?• Long term duplicate detection• Machine learning• Language detection 44
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  46. 46. 46http://www.mckinsey.com/en/Features/Big_Data.aspx
  47. 47. Agenda• Big data in context• Big structured data• Big unstructured data• Big opportunities and risks• Q&A 47
  48. 48. Big Data Opportunities• Improved visibility and insights – Can explore previously impractical questions• Real-time analytics – Less dependence on “dead data”• Blur the boundaries between structured and unstructured information – Unified views of resources and relations• Consolidation – Reduce the number of moving parts in your infrastructure • Along with related licensing and maintenance expenses• Compliance – capture and maintain data & records previously beyond firms capabilities 48
  49. 49. Big Data Risks• The potential for an ever-expanding set of information silos – Critical to relentlessly focus on minimized redundancy and optimized integration• GIGO (garbage in, garbage out) at super-scale – Dramatic improvements in capabilities and price/performance provide new opportunities for self-inflicted damage, for organizations that don’t model or query effectively• Cognitive overreach – The potential for information workers to create nonsensical queries based on poorly-designed and/or misunderstood information models• Skills gaps create competitive disadvantages 49
  50. 50. Q&APeter OKelly - peter@okellyassociates.comKathleen Reidy - kathleen.reidy@451Research.comHadley Reynolds - hadley.reynolds@nexteraresearch.com 50
  51. 51. Database market landscape Relational Analytic Mapr Infobright Netezza ParAccel SAP Sybase IQ Non-relational Piccolo Hadoop Teradata EMC IBM InfoSphere Dryad Brisk Greenplum Hadapt Aster Data Calpont VectorWise HP Vertica Operational Progress Oracle IBM DB2 SQL Server JustOne InterSystems MarkLogic MySQL Ingres PostgreSQL Objectivity Document Lotus Notes McObject SAP Sybase ASE EnterpriseDB Versant NoSQL CouchDB NewSQL HandlerSocket Akiban Key value MongoDB -as-a-Service MySQL Cluster Amazon RDS Couchbase RavenDB Cloudant App Engine SQL Azure Clustrix Riak Datastore Database.com Redis Drizzle Big tables Xeround FathomDB GenieDB Membrain SimpleDB ScalArc Cassandra Voldemort Hypertable Graph Schooner MySQL CodeFutures InfiniteGraph Tokutek ScaleBase NimbusDB BerkeleyDB HBase Neo4J Continuent GraphDB Translattice VoltDBData Grid/Cache Terracotta GigaSpaces Oracle Coherence Memcached IBM eXtreme Scale GridGain ScaleOut Vmware GemFire InfiniSpan CloudTran
  52. 52. Big Data Complexity Continuum Climate Modeling Gov’t Intelligence And Prediction Applications Predictions Trend Analytics MedicalNumber & Complexity of Technologies diagnostics Fraud Detection Influence Voice of Customer Networks Sentiment extraction Relationship Ad Targeting Reputation Retargeting Detection management Brand monitoring Intelligent Web search Machines Pattern Log Analysis Data mining eCommerce Detection Speech to text Time Historic Future(Predict) 52 Current (Monitor) Horizon IDC 2005
  53. 53. Big Data Characteristics Velocity Value Big Data Variety/ Volume Complexity© IDC 12/2/2011

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