MARC record from Internet Archive

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003 OCoLC
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019 $a862049864$a907212332$a961631341$a962643776
020 $a9781583473887$q(electronic bk.)
020 $a1583473882$q(electronic bk.)
020 $a9781583473863$q(electronic bk.)
020 $a1583473866$q(electronic bk.)
020 $z9781583473856
035 $a(OCoLC)868976998$z(OCoLC)862049864$z(OCoLC)907212332$z(OCoLC)961631341$z(OCoLC)962643776
037 $a6D23B698-43B2-4353-9A4B-9F5D46A61CA2$bOverDrive, Inc.$nhttp://www.overdrive.com
050 4 $aQA76.9.D3$bM85 2013eb
072 7 $aBUS$x041000$2bisacsh
072 7 $aBUS$x055000$2bisacsh
072 7 $aBUS$x059000$2bisacsh
082 04 $a650.02855369
100 1 $aMolaro, Cristian.
245 10 $aDB2 11 :$bdatabase for big data and analytics /$cCristian Molaro [and three others].
264 1 $aBoise :$bMC Press,$c[2013]
264 4 $c©2013
300 $a1 online resource (129 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
504 $aIncludes bibliographical references.
588 0 $aOnline resource; title from PDF title page (ebrary, viewed December 5, 2013).
505 0 $aFront Cover; Title Page; Copyright; Contents; About the Authors; Introduction by Surekha Parekh; DB2 11 for z/OS: Unmatched Efficiency for Big Data and Analytics by Julian Stuhler, Triton Consulting; DB2 11 for z/OS: The Database for Big Data and Analytics; Efficiency; CPU Reductions; zEC12 Exploitation; Application Compatibility; Transparent Archiving; Temporal Data Enhancements; Global Variables; Variable Arrays; Java Stored Procedure Enhancements; pureXML Enhancements; Optimizer and Query Performance Improvements; Data Sharing Performance Enhancements; Utility Enhancements.
505 8 $aOther Efficiency EnhancementsResilience; Extended Log Record Addressing: Current Issues; Extended Log Record Addressing: DB2 11 Enhancements; Enhanced Dynamic Schema Change; BIND/REBIND Enhancements; Security Enhancements; Other Resilience Enhancements; Business Analytics; SQL Aggregation Improvements; IBM DB2 Analytics Accelerator Enhancements; Hadoop and Big Data Support; QMF 11; Other Enhancements for Analytics Workloads; Upgrading to DB2 11; DB2 Version Prerequisites; Other Prerequisites; Upgrade Timing; Upgrade Process and Impact; DB2 11 Customer Case Studies; BMW Group.
505 8 $aStadtwerke Bielefeld GmbHJN Data; Improved Query Performance in DB2 11 for z/OS by Terry Purcell; Predicate Indexability; Duplicate Removal; Hash Join and Sparse Index; Page Range Screening and Indexing for Partitioned Table Spaces; RUNSTATS Enhancements; Additional Performance Improvements; Summary; IBM DB2 Utilities and Tools with DB2 11 for z/OS by Haakon Roberts; How DB2 for z/OS Can Help Reduce Total Cost of Ownership by Cristian Molaro; Business Needs and DB2 TCO; DB2 and TCO; Reducing TCO Through Synergy with System z; DB2 Synergy with System z; Reducing TCO Through CPU Savings.
505 8 $aDB2 10 CPU Savings and Performance ImprovementsDB2 10 Performance Expectations; DB2 11 CPU Savings and Performance Improvements; DB2 11 Performance Expectations; Specialty Engines; DB2 10 and Specialty Engines; DB2 11 and Specialty Engines; Estimating zIIP Savings; Special Considerations for High zIIP Utilization; DB2 and zAAP on zIIP; Reducing TCO with Better Performance; Identifying Better Performance Opportunities; Getting Better Performance with REBIND; DB2 Plan Management; DB2 11 APREUSE(WARN) Enhancement; DB2 11 RELEASE(DEALLOCATE) Optimization.
505 8 $aDB2 11 Application Compatibility and APPLCOMPATCase Study: Performance Benefits of REBIND; DB2 EXPLAIN At a Glance; DB2 10 High-Performance DBATs; Reducing TCO Through Storage Savings; DB2 Data Compression; DB2 Managed Disk Space Allocation; Case Study: Combined Effects of Data Compression and DB2 Managed Disk Space Allocation; Index Compression; Reducing TCO with Faster Analytics; IBM DB2 Analytics Accelerators; Leverage Legacy QMF Objects; Reducing TCO with Improved Scalability; DB2 10 Throughput Enhancements; DB2 Storage and Scalability; Data Sharing Member Consolidations; Conclusion.
520 $aThe landscape of today's business is shaped by the mountains of data being produced, with rapid growth in the volume, variety, and velocity of data due to the explosion of smart devices, mobile applications, cloud computing, and social media. Much of this growth has been in unstructured data; however, by 2020, internet business transactions-business-to-business and business-to-consumer-are predicted to reach 450 billion per day. Smart organizations are seeking innovative ways to turn this explosion of data, called big data, i.
630 00 $aIBM Database 2.
650 0 $aDatabase management.
650 0 $aRelational databases.
650 6 $aBases de données$xGestion.
650 6 $aBases de données relationnelles.
650 7 $aBUSINESS & ECONOMICS$xManagement.$2bisacsh
650 7 $aBUSINESS & ECONOMICS$xReference.$2bisacsh
650 7 $aBUSINESS & ECONOMICS$xSkills.$2bisacsh
630 07 $aIBM Database 2.$2fast$0(OCoLC)fst01367762
650 7 $aDatabase management.$2fast$0(OCoLC)fst00888037
650 7 $aRelational databases.$2fast$0(OCoLC)fst01093575
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aMolaro, Cristian.$tDB2 11 : The Database for Big Data & Analytics.$dChicago : MC Press, ©2013$z9781583473856
856 40 $3ebrary$uhttp://site.ebrary.com/id/10796499
856 40 $3EBSCOhost$uhttps://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=656785
856 40 $3MyiLibrary$uhttp://www.myilibrary.com?id=538386
856 40 $3OverDrive$uhttps://www.overdrive.com/search?q=6D23B698-43B2-4353-9A4B-9F5D46A61CA2
856 40 $3ProQuest Ebook Central$uhttps://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=1507337
856 4 $3Excerpts$uhttps://samples.overdrive.com/db2-11?.epub-sample.overdrive.com
856 4 $3Image$uhttp://images.contentreserve.com/ImageType-100/0427-1/{6D23B698-43B2-4353-9A4B-9F5D46A61CA2}Img100.jpg
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938 $aProQuest MyiLibrary Digital eBook Collection$bIDEB$ncis26538177
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