Understanding the SAP HANA Difference. Amit Satoor, SAP Data Management

Transcription

1 Understanding the SAP HANA Difference Amit Satoor, SAP Data Management

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3 The future holds many transformational opportunities Capitalize on the new technology frontier Retail: From transactions to 1:1 engaging relationships Manufacturing: From mass production to custom 3-D printing Healthcare: From generic treatments to personalized medicine 2013 SAP AG. All rights reserved. 3

4 SAP HANA Difference Enabling real-time computing design patterns across entire software architecture SIMPLIFIED OLTP + OLAP in Columnar database CONVERGED End-to-end Data Processing OPTIMIZED Application Processing SAP HANA (Main Memory) Operational Analytics Predictive Machine Learning Prescriptive Sentiment Intelligence SAP HANA (Main Memory) Libraries SAP HANA (Main Memory) In-Memory Database layer Application Layer Sensors Transactions Spatial/GIS Database & data processing engines Application Server Image Text Integration Services Development, Deployment and Administration 2013 SAP AG. All rights reserved. 4

5 Uncover value Create breakthroughs Experience simplicity INNOVATIONS PREVIOUSLY UNFEASIBLE Real-time genome analysis Instantaneous fraud detection Predictive maintenance Optimize procurement, manufacturing, transportation Real-time MRP with instant re-planning VALUES PREVIOUSLY UNATTAINABLE Iterative period end closing Cash forecasts/management Real-time offer calculation In-moment sales forecast Self-service apps with instantaneous response Interactive POS data analysis SAP HANA In-Memory Transaction & Analysis directly In-Memory SIMPLICITY PREVIOUSLY UNACHIEVABLE Transactions and analysis in one system Efficiently analyze structured and unstructured data Fewer systems needed Hardware cost savings Less DBA involvement needed 2013 SAP AG. All rights reserved. 5

6 Building next generation apps with SAP HANA John Global Head of SAP HANA

7 What is SAP HANA?

8 What is SAP HANA? SAP HANA is a re-imagined platform for business applications Designed from the ground up Not limited by 30 years of database legacy Designed for modern multi-core computers SAP HANA includes the whole application platform in-memory Database Services Text Analysis and Search Event Processing Predictive, Graph and Spatial Engines Integration/Web Services SAP HANA is Enterprise Ready High Availability, Disaster Recovery, Backup/Restore, ACID Compliant Security Compliant (e.g. HIPAA) Repository, User and Version Management 8

9 The structure of future applications We believe that future applications will span domains, in real-time Transactional Data Internet of Things Suppliers Customer Invoice Product Employee Sales Order Reference Data Social/News 9

10 Challenges of a traditional RDBMS

11 Oracle Stack 11

12 Microsoft Stack 12

13 IBM Stack 13

14 SAP HANA 14

15 Real-Time Applications

16 Being able to transact in real-time Consuming transactional data Tested at up to 250k transactions/sec in a bank Stored only once No Indexes No Aggregates No Materialized Views No Duplication or ETL Dramatic reduction in data footprint Up to 20x for redesigned apps Normally 5x for re-platformed app Reduced data footprint = simplicity Dramatic reduction in cost to build and maintain 5-20x less developer effort 16

17 and report in real-time SAP HANA Information Views built on base data 2bn scans/sec/core, 16m aggregations/sec/core 40% more with Intel Ivy Bridge, 50% more cores 750m aggregations/sec with 1 40-core system Most CPU time spent in Data Mart is on ETL Aggregates are not required in SAP HANA Instead, CPU time spent calculating what is needed 17

18 Consuming Reference Data

19 Public reference data is everywhere Most governments have an active data program Many public and private organizations have the same If you need it it s probably available Most reference sources are free of charge 19

20 NOAA Temperature and Rain data NOAA NCDC data is 140m measurements per annum 4GB/year stored in SAP HANA stored only once 20

21 We create re-usable information views 21

22 Good performance Even aggregating all our weather data, 2.4bn rows 1-2 seconds 22

23 Performance improves as we filter Performance always improves as we filter This model can be joined into other models in SAP HANA system Or consumed from another SAP HANA system via Smart Data Access 23

24 Consuming Social & Sensor Data

25 Social and Sensor data is everywhere Almost everything has a sensor Most sensors have an API Most APIs are publicly accessible Usually OAuth and OData compliant Easily integrated into SAP HANA 25

26 Consuming Twitter/News with SAP HANA Using python it is straightforward to integrate APIs into SAP HANA Specific keywords (products, companies, people) can be tagged Sentiment analysis possible (see next section) 26

27 Text & Sentiment Analysis

28 Consuming Text Storage and analysis of Text data straightforward Either in PDF/Text form in a large database object (up to 2GB) Or consumed from social/news feeds Both Search and Sentiment is possible from one text index Text indexes are built asynchronously 28

29 Building a Text Index in SAP HANA One simple command: Physically creates a table $TA_VOICE 1m rows, just 50mb 29

30 Consuming Text Indexes Text Analysis is very powerful Language Sentiment Token (Keyword) Type e.g. Sentiment, Weapon, Emoticon Queried like any other DB table Joined into an Information Model 30

31 Text Indexes into Information Views Now we can consume our Text Index into an Information View Now it is part of our calculation model which we can consume externally 31

32 Simple Info Access (SInA) Note we can also consume text indexes into JavaScript Allows for Google-style searching 32

33 Predictive Analysis Library

34 SAP HANA Predictive Analysis Library PAL can be used to write predictives in-line with applications Providing the most popular predictive algorithms Performance is typically excellent (1-5 seconds) even on big datasets 34

35 SAP HANA Predictive - Integration We can use SAP HANA Information Models to run PAL algorithms against real-time data In this example we do association analysis between customer and merchant 35

36 SAP HANA Web Services (XS)

37 SAP HANA XS Provides a lightweight web server Server-Side JavaScript or OData Scalable and Enterprise-Class Repository with versions and users 37

38 D3 JavaScript Libraries Easily consumed into SAP HANA XS Connect to SAP HANA XS OData Services or Server Side JavaScript 38

39 SAP UI5 Installed on your SAP HANA Appliance Provides the ability to build rich UI applications out the box 39

40 SAP HANA UIS SAP HANA UIS provides the ability to build widgets and pages very quickly Very useful for Analytics apps, which are easy to build in SAP HANA 40

41 SAP River

42 SAP River development language Included with SAP HANA SPS7 Rapid, descriptive language Combined with SAP HANA Views OData Compatible SAP HANA XS for development Build apps in days, not months 42

43 Example Applications

44 Retail Customer Analytics Built on real-time POS data Aggregated on the fly based on inputs 44

45 Retail Customer Analytics Use of D3 JavaScript Libraries 45

46 Influencer Analysis Built in SAP River and Lumira in 1 day 46

47 Influencer Analysis Consumes both structured and unstructured data in one model 47

48 Questions? John bluefinsolutions.com/johnappleby 48

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