Let s distribute.. NOW: Modern Data Platform as Basis for Transformation and new Services

Transcription

1 Let s distribute.. NOW: Modern Data Platform as Basis for Transformation and new Services Matthias Kupczak, Michael Probst; SAP June, 2017

2 Agenda 09:30-09:55 Coffee 09:55-10:00 Welcome Message T-Systems 10:00-10:15 Introductions 10:15-11:15 SAP Modern Data Platform, HANA Vora Overview & Positioning 11:15-11:45 Prepare the landscape (Connectivity, Startup) 11:45-12:45 Lunch 12:45-14:45 SAP Vora Hands on Übung 14:45-15:00 Break 15:00-15:30 T-Systems project experiences: Best Practice 15:30-15:45 Summary, Product Outlook, Use Cases 15:45-16:15 Open Discussion & Meet the experts 2

3 SAP Modern Data Platform

4 IoT creates a big data problem that analytics must solve transform huge volumes of data into a small quantity of usable/actionable information. - Peter Middleton, Gartner 4

5 SAP product portfolio in a nutshell: the modular suite Cross-application integration & common design SAP Fiori design paradigm SAP Cloud Integration & APIs Standard Business Applications Business Intelligence Extensions & Custom Applications S/4HANA Cloud ed. Business Objects Cloud & Digital Boardroom Extensions & Custom Apps Customer 3 rd Party Apps App Center Cloud Apps S/4HANA Stable digital core BW/4HANA On Premise Historical reporting & analytics Microservices API Business Hub Security Integration Mobile UX Collaboration Analytics SAP HANA Platform SAP Cloud Platform SAP Cloud Platform SAP Vora Machine Learning IoT External data sources Big Data Service 5

6 Spend Management MODE 1 MODE 2 Custom Applications SAP Ariba & Concur Industry Solutions MODERN DATA PLATFORM Microservices Customer Specific & 3rd Party Digital Core BUSINESS PROCESS BUSINESS INTELLIGENCE IoT SAP Clea SAP Leonardo SAP SuccessFactors, SAP Fieldglass SAP Hybris Workforce Engagement SAP Digital Boardroom Analytics & Insight Predictve Customer Experience

7 Spend Management MODE 1 MODE 2 Custom Applications SAP Ariba & Concur Modern Data Platform Microservices Digital Core Industry Solutions SAP Clea SAP SuccessFactors, SAP Fieldglass SAP BW/4HANA & Mixed Processing Services SAP HANA SAP VORA SAP Data Hub SAP HANA Native / Data Mart Integration Services Database Services Big Data & Data Lake Application Services SAP Cloud Platform SAP Big Data Services Customer Specific & 3rd Party SAP Leonardo SAP Hybris IoT Workforce Engagement SAP Digital Boardroom Analytics & Insight Predictve Customer Experience

8 Why Hadoop

9 Budget Data

10 Costs is onle ONE reason Low Cost & Large Scale Schema Flexibility Any Data 10

11 New Business Opportunity Operational Excellence Channel Process Business Models Recomendation IoT Community Networks Cost Customer 11

12 Data being the driver for Transformation and new Services

13 When we started it was very much about the product. It was about the shirts and shoes. Now it s much more about the digital product. The reality is that the physical product and the digital product are not distinguishable. Kurt Kendall, VP Analytics and Data Solutions at Under Armour 13

14 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 14

15 2015 SAP SE or an SAP affiliate company. All rights reserved. Internal 15

16 Utility Company: Predictive Maintenance & Asset Health Monitoring 16

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18 Bosch Siemens Home Appliances (B/S/H) Home Connect Home Connect is a connected household platform to control and monitor home appliances of different brands via one single app, accessing several smart services. Home Connect is a digital ecosystem which intends to integrate further brands, technology partners and developers to continuously expand the range of valueadding services. Home Connect is currently available for ovens, fridges, coffee makers, dishwashers, washing machines, and dryers. 18

19 Bosch Siemens Home Appliances (B/S/H) Process & Use Case Overview Data Collection Data Analysis Data Utilization Data Lake Product Ideas Customer Home Connect Data Data Analyst Product Management Production Dish Washer Customer Data Customer-oriented Dish Washer Collection of sensor-/machine data Collection of user behavior of customers Analysis of sensor-/machine data for pattern detection and knowledge acquisition Which customers use which wash cycle (program)? Improvement of product quality Product management can consider the analyzed data for improving the products with regard to design/functions and production Customers herewith have a direct influence on the product quality 19

20 Data Driven Applications & Insights

21 From Process Driven to Data Driven Applications Process Driven Applications Process Trigger Data Person Intelligence Machine Data Driven Applications Internal, Business Partner Data Source + New, External 21

22 Data Driven Applications: From Insight to Action Process Driven Applications Data Driven Applications BUSINESS PROCESS BUSINESS INTELLIGENCE Internal, Busi ness Partner New, External Change Understand 22

23 New challanges require new technologies Distributed systems in a distributed landscape Existing Systems Hadoop/Spark Cloud Storage (i.e. AW S S3) Machine Learning (Python, Spark, Tensorflow) 23

24 Gartner: Still challenging to get Hadoop projects into production stage (1) Skills (2) Technology Integration Challenges (3) Determining Value Gartner, 2017 (ID: G ) 24

25 Platform for Data Driven Applications & Insights

26 Simplify Technology Integration Challenge SAP Data Hub Data Driven App SAP HANA Monitoring SAP Vora Hadoop Exploration & BI Orchestration Cloud Storage BW Data Management & Preparation Machine Learning Existing Systems Distributed Data Systems 26

27 From Data to Data Driven Applications LAND REFINE CONSUME ACT # Join Filter Cleanse Look-up Script Mask Anonymize Parse $ %&? Spark Job Python Script # SAP DS HDFS S3 Data Hub Vora HANA BW Apps 27

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29 HANA as a Semantic Layer We re going to keep a copy of all of our data in Hadoop Maintain our current Oracle DWs, at least as a staging area Push high-value data directly in HANA Use HANA as a semantic layer, no matter where the data physically resides Keep the last six months in HANA, six months before that in Dynamic Tiering, the six years before that in Hadoop Even if we don t store it in HANA, we ll still model it in HANA. So we can use it with all of our Tools 29

30 From Data to Data Driven Applications LAND REFINE CONSUME CURATE # Join Filter Cleanse Look-up Script Mask Anonymize Parse $ %&? Spark Job Python Script # HDFS S3 Data Hub Vora HANA BW Apps 30

31 Data Driven Applications Add Business Context Gain New Insight

32 COMPUTE Sources Ingest Data Store Consume Vora and Hadoop Where do they fit in the landscape? GBs - TBs TBs - 10s of TBs 10s of TBs - PBs Industry Apps System of Record In- Memory Next Generation Data Warehouse HANA / BW/4HANA In- Memory Data Tiering Data Lake SAP Vora Hadoop and Spark On-Premises In the Cloud Structured data for fast analytics Less frequently accessed, structured data Raw data: semi-structured, unstructured, streaming data etc. Smart Data Streaming Data Services Log Data Sensor data Machine Data 32

33 Simplify: Use Existing Skill Sets Business Analyst Business End User Enable Business Users to Act On Digestable insight With no need to learn new skills App Developer Data Scientist Data Engineer Operations Tools to use existing skill Collaboration between existing IT users & Hadoop experts Provide data scientists with the data access they need 33

34 Key Benefits of SAP HANA Vora Adding value to Hadoop Database in Hadoop Multiple Engines Intuitive Tools Native HANA Integration Vora Vora Vora Relational (OLAP) Graphs HANA 0.1sec Vora Vora Vora Vora Vora Vora In-Memor Time Series Doc Store Hadoop Distributed compute & Massive Scale Out Relational & NoSQL Increase Productivity & Use Existing Skills Fast Apps & BI & HANA-Hadoop Integration 34

35 SAP HANA Vora: Distributed Computing Solution for the Digital Enterprise Distributed computing cluster Vora Vora Vora Spark Spark Spark Data Science, Predictive, Business Intelligence, Visualization Apps Files Files Files Hadoop SAP HANA Vora Data Modeler Relational Time Series Graph Doc Store Disk-to-Memory Accelerator Distributed Transaction Log Spark Hadoop 35

36 Whiteboard 36

37 Part 2 Accessing Vora from SAP HANA 40 Minutes Connecting Vora artefacts from SAP HANA SAP HANA Remote Source / Virtual Table / HANA Calculation View Part 1 Working with Vora Part 3 Working with the Vora Modeller 40 Minutes 40 Minutes 37

38 Summary & Product Outlook

39 ROADMAP PLACEHOLDER (request to latest roadmap pending ) Extern gibt es nur eine sehr alte: orm-technology.html SAP VOra 39

40 Strategy & Infrastructure TECHNOLOGY PEOPLE PROCESS 40

41 3-4-3 M e thodology IDENTIFY IMAGINE ENABLE EXECUTE

42 And now what? Vora Test Drive on AWS HANA Academy on YouTube (search for: Vora) Open SAP Course (link) Self Learning Design Thinking SAP Tech Academy 42

43 Thank you. Contact information: Matthias Kupczak Pre Sales Engineer Michael Probst Solution Advisor, BigData