Roundtable 1: Relational and Analytic Database Innovations

Wednesday, February 22, 12

Eric Kavanagh Eric.kavanagh@bloorgroup.com Twitter Tag: #briefrWednesday, February 22, 12

To conduct an Open Research program that invites the participation of both IT users and technology vendors To assist IT buyers in understanding database technology and the architecture that surrounds it. Allow audience members to pose serious questions... and get answers! Publish all findings Twitter Tag: #briefrWednesday, February 22, 12

Your Host: Eric Kavanagh Research Leader: Mark Madsen - Third Nature Primary Collaborator: Robin Bloor - The Bloor Group Guest Analyst 1: Rajiv Rawat - BI Results Guest Analyst 2: Malcolm Chisholm - ConsultantWednesday, February 22, 12

Rajeev Rawat is the founder and CEO of BI Results. His career has involved leading large cross-functional teams at both IBM and Xerox, where he was involved in direct customer facing roles as well as taking part in headquarters assignments. His headquarters positions with worldwide responsibility included strategic assignments for alliances and relationships with technology partners, product management and product marketing. Other responsibilities include restructuring business models, test of new technology platforms, and sales coverage plans. Rajeev led the introduction of new technologies and solutions for Xerox and IBM. www.biresults.com, biresult@gmail.com LinkedIn: Rajeev Rawat Twitter Tag: #briefWednesday, February 22, 12

The Bloor Group Fit for Purpose: The New Database Revolution The Bloor Group – February 22, 2012 Five Years of Incredible Excitement In Information Acrobatics! -Seismic shift in dataÇ Ç Variety, Volume, Velocity√ √ Rajeev Rawat Serving to achieve your full potential ©Copyright BI Results, LLC 2012 Wednesday, February 22, 12

The Bloor Group The Next Five Years The Most Exciting Times In Information Acrobatics New Venture Funding Key Value Store, Big Table, Graph DB, Document DB New (Needed) FunctionalityÇ Ç New Skills√ √ New Ventures Innovative Code NoSQL Innovation Lots of Great Innovation Apache Project, Amazon, Facebook, Google, Open Source Community, Twitter Reports of the Death of The RDBMS Are Highly Exaggerated ©Copyright BI Results, LLC 2012 Wednesday, February 22, 12

The Bloor Group RDBMS Still Dominates Reliable Heavy Lifting RDBMS Vs. NoSQL? Strengths - Robust (ACID, Fail-proof) - Structure (Granular, Scalable, Fast)Ç - Governance (Backups, Precision) - Tools (ETL, Analytics, Reporting) Ç√ - Ecosystem (Global deep collaboration) - Skills (Certiﬁcations, Experience) √ - Policies, Procedures (Reliability) - Documentation (Support, Training) Photo: Watchmojo.com Reports of the Death of RDBMS Are Highly Exaggerated ©Copyright BI Results, LLC 2012 Wednesday, February 22, 12

The Bloor Group NoSQL Being Tested, Validated, Calibrated Key Value Store, Big Table, Graph - Co-Existence, Transition, DB, Document DB NoSQL Only - Meta Tag, Master DataÇ Ç Other scheme/s√ √ - Data Governance, Controls. Authentication, Security - Deep Analytics on Mixed Datasets Complexity, Semi- Structured, Highly Connected Data Fantastic Growth Opportunity Skills, Investing ©Copyright BI Results, LLC 2012 Wednesday, February 22, 12

The Bloor Group NoSQL, RDBMS Innovation Fantastic Opportunity for Growth Gaps You Can Help Close The Race Is On! - Mapping Big Data with Legacy DataÇ - Strategy and Policy for Finish Line Ç√ √ Governance, Precision, Controls - Opportunities at all sides - Enterprise - Legacy Vendors - Innovative Ventures Tested For Prime Time - Technology and Business Time to Rise To The Top Skills, Investing ©Copyright BI Results, LLC 2012 Wednesday, February 22, 12

Disection & Discussion Twitter Tag: #briefrWednesday, February 22, 12

Robin Bloor is Chief Analyst at The Bloor Group. Robin.Bloor@Bloorgroup.com Twitter Tag: #briefrWednesday, February 22, 12

RDBMSWednesday, February 22, 12

The SQL Barrier SQL has: DDL (for data deﬁnition) SQL Barrier DML (for Select, Project and Join) Results Or results But it has no MML or TML processing must be done here processing must be done here Usually result sets are brought to the client for further manipulation, but using them for further data access becomes problematic. SQL Conclusions: Analytic DBMS This separation of data from process is arbitrary and unhelpfulWednesday, February 22, 12

That MapReduce Thing There are two fundamental approaches to parallelism Data Partitioning Process partitioning MapReduce implements an approach which is oriented to the ﬁrst of these. Thus proves to be suited to many “big data” tasks. It is not the end ofd the parallel processing story by any means.Wednesday, February 22, 12

Malcolm Chisholm has 25+ years experience in data management working in finance, insurance, manufacturing, government, defense, pharmaceuticals, and retail. He specializes in data governance, MDM, metadata engineering, business rules management/execution, data architecture and design. He is a well-known presenter at conferences in the U.S. and Europe, writes columns in trade journals, and has authored the books: Managing Reference Data in Enterprise Databases; How to Build a Business Rules Engine; and Definitions in Information Management. In 2011, Malcolm was presented with the prestigious DAMA International Professional Achievement Award for contributions to Master Data Management. He can be contacted at mchisholm@refdataportal.com. Twitter Tag: #briefrWednesday, February 22, 12

The New Database Revolution: Relational Roundtable The Virtual Circle February 22, 2012 San Francisco Malcolm Chisholm Ph.D. mchisholm@refdataportal.com Telephone 732-687-9283 • Fax 407-264-6809 www.refdataportal.com www.bizrulesengine.com © AskGet.com Inc., 2012. All rights reservedWednesday, February 22, 12

“Big Data” Is Used Differently Relational Paradigm ULS Dataspace in Cloud “Set at a time” processing Uncover individual facts Behavior of populations of identical things Much is master data Event data predominates Events are not as much repetitive transactions Exception reporting for singular things/events Can aggregate from individual facts (but bottom- (bust still top-down) up) Heavy data entry supported Surf and drill Data entry is to support analysis • The relational paradigm is different to ULS “Big Data”. [ULS = Ultra-Large Scale - usually Petabyte scale] • Difficult to rely on relational thinking in Cloud databases © AskGet.com Inc., 2012. All rights reservedWednesday, February 22, 12

Sources Source A Emails Source B Documents I ULS Dataspace in Cloud Web Pages N Source C G XML E Source D Relational S T Flat Files I Source E O Audio N Image Video • Sources provide data to the ULS dataspace • One source can provide many data formats • Many sources can provide the same format • Sources may duplicate the same data • HINT – Think metadata © AskGet.com Inc., 2012. All rights reservedWednesday, February 22, 12

Segments in Dataspace ULS Dataspace in Cloud Source A I N Ingested Data Terms in G Store Documents Document-Term Source B E Inverted Index S M/R M/R Source C T I O Source N N Extracted Deduplicated Master Data Master Data M/R M/R • The ULS dataspace is not a single “blob” of data • It will have different segments with different kinds of data in it • The segments will be derived from the originally ingested data • MapReduce (M/R) is the equivalent of ETL to move data around and transform it (filter, summarize) © AskGet.com Inc., 2012. All rights reservedWednesday, February 22, 12

No Common Notation for Columnar Designs ? Col A Col B Col C Col D Col E Row 01 Val1A Row 02 Val2A Val2B Val2C Val2D Val2E Row 03 Val3A Val3C Val3E • E/R diagramming techniques allow us to visualize a relational database • There is nothing that is quite the same for columnar databases • (a) It is sparse and columns may be missing • (b) How do you show the MapReduce transformations (not quite relations)? © AskGet.com Inc., 2012. All rights reservedWednesday, February 22, 12

Need a Data Dictionary • The ULS dataspace can grow quickly and have many data objects • Without a DD developers and users will get hopelessly lost (none of the logic imposed by the relational model) • The fundamental unit is the field – show where it occurs in rows, ColQuals and payloads • Tables less important than in relational © AskGet.com Inc., 2012. All rights reservedWednesday, February 22, 12

Mark Madsen is founder of Third Nature, a research and consulting firm focused on analytics, BI and decision-making. Mark spent the past two decades working on analysis and decision support in many industries and countries. He is an award- winning architect and former CTO whose work has been featured in numerous industry publications. Over the past ten years Mark received awards for his work from the American Productivity & Quality Center, TDWI, and the Smithsonian Institute. He is an international speaker, a contributing editor at Intelligent Enterprise, and manages the open source channel at the Business Intelligence Network. For more information or to contact Mark, visit http:// ThirdNature.net. Twitter Tag: #briefrWednesday, February 22, 12

One Size Doesn’t Fit All February 22, 2012 Mark R. Madsen http://ThirdNature.netWednesday, February 22, 12

The future of data is the databaseWednesday, February 22, 12

You keep using that word. I do not think it means what you think it means.Wednesday, February 22, 12

Good conceptual model, but a prematurely standardized implementa5onThe rela*onal database is the franchise technology for storing and retrieving data, but…1.Global, sta*c schema model2.No rich typing system3.Many are not a good ﬁt for network parallel compu*ng, aka cloud4.Limited API in atomic SQL statement syntax & simple result set returnWednesday, February 22, 12

Plus, if they’re all the same why are there so many? Sybase IQ, ASE EnterpriseDB Algebraix Teradata, Aster Data LucidDB Intersystems Caché Oracle, RAC Vectorwise Streambase MicrosoT SQLServer, PDW MonetDB SQLStream IBM DB2s, Netezza Exasol Coral8 Paraccel Illuminate Ingres Kogni*o Ver*ca Postgres EMC/Greenplum InﬁniDB Cassandra Oracle Exadata 1010 Data CouchDB SAP HANA SAND Mongo Infobright Endeca Hbase MySQL Xtreme Data Redis MarkLogic IMS RainStor Tokyo Cabinet Hive Scalaris And a few hundred more.Wednesday, February 22, 12

The future of data is the rela0onal database? SQL noSQLWednesday, February 22, 12

Technologies are not perfect replacements for one another. When replacing the old with the new (or ignoring the new over the old) you always make tradeoﬀs, and usually you won’t see them for a long 0me.Wednesday, February 22, 12

March: Vendor Research March 14th: Second Round Table focusing on No SQL databases and their application DB Revolution Survey conducted April: Vendor Research Publishing of Round Table Transcripts, with comments May: Authoring of White Paper Publishing of White Paper Publishing of survey activity Twitter Tag: #briefrWednesday, February 22, 12

March 14th: Second DB Revolution Round Table March Briefing Room: Integration April Briefing Room: Discovery May Briefing Room: Analytics Twitter Tag: #briefrWednesday, February 22, 12

Thank You For Your AttentionWednesday, February 22, 12

Roundtable 1: Relational and Analytic Database Innovations

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Roundtable 1: Relational and Analytic Database Innovations Presentation Transcript