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TDWI Roundtable: The HANA EDW
 

TDWI Roundtable: The HANA EDW

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- What is SAP HANA?

- What is SAP HANA?
- Technology behind SAP HANA
- Data Warehousing on SAP HANA
- How HANA and Business Warehouse (BW) fit together

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    TDWI Roundtable: The HANA EDW TDWI Roundtable: The HANA EDW Presentation Transcript

    • © 2013 SAP AG. All rights reserved. 1The HANA EDWDr. Thomas ZurekVP HANA Business WarehouseJune 2013
    • © 2013 SAP AG. All rights reserved. 2DisclaimerThis presentation outlines our general product direction and should not be relied onin making a purchase decision. This presentation is not subject to your licenseagreement or any other agreement with SAP. SAP has no obligation to pursue anycourse of business outlined in this presentation or to develop or release anyfunctionality mentioned in this presentation. This presentation and SAPs strategyand possible future developments are subject to change and may be changed bySAP at any time for any reason without notice. This document is provided without awarranty of any kind, either express or implied, including but not limited to, theimplied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in thisdocument, except if such damages were caused by SAP intentionally or grosslynegligent.
    • Agenda1. Challenges in Data Warehousing2. Technology Trends3. What is SAP HANA ?4. Demo
    • Agenda1. Challenges in Data Warehousing2. Technology Trends3. What is SAP HANA ?4. Demo
    • © 2013 SAP AG. All rights reserved. 5A Typical Data Warehouse Architecture
    • © 2013 SAP AG. All rights reserved. 6EDW = DB + X* modeling analytic models DW containers data flows transformations security conventions / standards (e.g. naming) lifecycle of models + data incl. impact analysis + propagation online, nearline, offline data scheduling + monitoring data integrity + compliance extraction + connectivity SQL storage / persistence ACID partitioning indexing clustering processing engines calculations aggregation planning data mining / predictive search back-up, recovery, fail-over* X is an EDW application (= set of scripts, programs, tools) running on top of the DB
    • © 2013 SAP AG. All rights reserved. 7The Data Warehousing Quadrantdatavolumehugemodestnumber of data models, sources, …modest huge internet scale business process(e.g. Ebay, Amazon, …) generatinghuge amounts of (sensor) data fairly modest challenges regardingsemantics, consolidation, harmoni-zation, integration with other data few data sources mix of scenarios with small andlarge amounts of data many (1000s to 10000s) of datamodels many (100s to 1000s) different datasources data mart type of setup oroperational (OLTP) analytics modest number of tables modest (need for) integrationsbetween data modelsVLDW XLDWEDWData Mart more scenarios more combinationsof scenariosmoregranulardatasensor/bigdatamorescenariosBWHANA
    • © 2013 SAP AG. All rights reserved. 8The HANA EDWBlog: http://tinyurl.com/hana-edw
    • Agenda1. Challenges in Data Warehousing2. Technology Trends3. What is SAP HANA ?4. Demo
    • © 2013 SAP AG. All rights reserved. 10Key Impacts on Modern DBMSAdvances in Technology column-store in-memory multi-core processors data compression infiniband hard- and software bundling NoSQL (i.e. no-ACID) …Application-Awareness DB tailored towards the applications providing generic operations frequently used by those applications not in standard SQL (or else) examples currency conversion unit of measure conversion hierarchy logic delta management  BWs DSO calculation engine planning engine
    • © 2013 SAP AG. All rights reserved. 11Key Impacts on Modern DBMSAdvances in Technology column-store in-memory multi-core processors data compression infiniband hard- and software bundling NoSQL (i.e. no-ACID) …Application-Awareness DB tailored towards the applications providing generic operations frequently used by those applications not in standard SQL (or else) examples currency conversion unit of measure conversion hierarchy logic delta management  BWs DSO calculation engine planning engine
    • © 2013 SAP AG. All rights reserved. 12CoreCPUBottleneck today:CPU waiting for data to beloaded from memory into cacheBottleneck in the past:Disk I/ODiskCPU CacheMain MemoryIn-Memory ComputingType of Memory Size Latency (~)L1 CPU Cache 64K 1 nsL2 CPU Cache 256K 5 nsL3 CPU Cache 8M 20 nsMain Memory GBs up to TBs 100nsDisk TBs >1.000.000 ns
    • © 2013 SAP AG. All rights reserved. 13Key Impacts on Modern DBMSAdvances in Technology column-store in-memory multi-core processors data compression infiniband hard- and software bundling NoSQL (i.e. no-ACID) …Application-Awareness DB tailored towards the applications providing generic operations frequently used by those applications not in standard SQL (or else) examples currency conversion unit of measure conversion hierarchy logic delta management  BWs DSO calculation engine planning engine
    • © 2013 SAP AG. All rights reserved. 14In-Memory PlanningSimple Disaggregation ExampleTraditional Approach1. Determine the delta  +502. Disaggregate (in appl. server) per week (52) per branch (500) 26000 combinations / values3. Send 26000 values to DB to saveHANA-Based Approach1. Determine the delta  +502. Send 1 value to DB+ instruction to disaggregate andhow3. Disaggregate (in DB engine) per week (52) per branch (500) create + save 26000 valuesFY 2010actualFY 2011planFrance 200 200Germany 250 250Italy 180 180FY 2010actualFY 2011planFrance 200 200Germany 250 300Italy 180 180user changesa plan value
    • © 2013 SAP AG. All rights reserved. 15In-Memory PlanningSimple Disaggregation ExampleTraditional Approach1. Determine the delta  +502. Disaggregate (in appl. server) per week (52) per branch (500) 26000 combinations / values3. Send 26000 values to DB to saveHANA-Based Approach1. Determine the delta  +502. Send 1 value to DB+ instruction to disaggregate andhow3. Disaggregate (in DB engine) per week (52) per branch (500) create + save 26000 valuesFY 2010actualFY 2011planFrance 200 200Germany 250 250Italy 180 180FY 2010actualFY 2011planFrance 200 200Germany 250 300Italy 180 180user changesa plan value
    • © 2013 SAP AG. All rights reserved. 16General Pattern: Data-to-Code vs. Code-to-Data
    • Agenda1. Challenges in Data Warehousing2. Technology Trends3. What is SAP HANA ?4. Demo
    • © 2013 SAP AG. All rights reserved. 18SAP HANASoftware Component ViewStandard and specialized interfacesSQL SQL Script MDX OtherText AnalyticsAppl. Function LibrariesParallel Calculation engineRelational StoresObject Graph StoreManaged ApplianceBusiness Function LibraryPredictive Analysis LibraryRow basedColumnar Application logic extensions Parallel data flow computing model Multiple in-memory stores Appliance Packaging
    • © 2013 SAP AG. All rights reserved. 19SAP HANA Deployment OptionsSupported Scenariosbare metal• Single server• Scale-out / HAclusterCloud• HANA Enterpriseedition• HANA ONEpremiumVirtualized• VMware vSphereCloud• HANA developereditionOn-premise/private productivenon-productivevirtualprivate/public
    • Agenda1. Challenges in Data Warehousing2. Technology Trends3. What is SAP HANA ?4. Demo
    • © 2013 SAP AG. All rights reserved. 21Gross Profit Margin Bridge – DemoAnalyze a change from one period to anotherBaseProfitNewProfitSalesVolumeEffectProductMixEffectPriceEffectCostEffectOthers
    • © 2013 SAP AG. All rights reserved. 23HANA References SAP HANA: http://www.saphana.com SAP Help on HANA: http://help.sap.com/hana BW-on-HANA:http://www.saphana.com/community/learn/solutions/net-weaver-bw