#asksap Analytics Innovations Community Call: SAP BW/4HANA - the Big Data Warehouse for the Digital Enterprise

1. #askSAP Analytics Innovations Community Call Oct 26, 2017 SAP BW/4HANA The Big Data Warehouse for the Digital Enterprise

2. 2© 2017 SAP SE or an SAP affiliate company. All rights reserved. • Quarterly series for the Analytics community hosted by SAP • An opportunity for you to direct the discussion, get your questions answered, and end the session with some useful advice • Live and interactive 90 minutes • Connect on topics before, during, and after the call via Twitter using #askSAP #askSAP Analytics Innovations Community Call Series

3. 3© 2017 SAP SE or an SAP affiliate company. All rights reserved. Legal Disclaimer The information in this presentation is confidential and proprietary to SAP and may not be disclosed without the permission of SAP. This presentation is not subject to your license agreement or any other service or subscription agreement with SAP. SAP has no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation and SAP's strategy and possible future developments, products and or platforms directions and functionality are all subject to change and may be changed by SAP at any time for any reason without notice. The information in this document is not a commitment, promise or legal obligation to deliver any material, code or functionality. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. This document is for informational purposes and may not be incorporated into a contract. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP´s willful misconduct or gross negligence. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

4. 4© 2017 SAP SE or an SAP affiliate company. All rights reserved. Today’s Speakers Eric Leicht Enterprise Data Warehousing Architect J.J. Keller Andy Bitterer Chief Evangelist, Analytics SAP Glen Leslie Analytics Architect SAP Vamsi Paladugu Director, Data and Analytics Katerra Marc Hartz Lead Product Manager, Big Data SAP The Changing Landscape of Data Warehousing SAP BW/4HANA Vision and Strategy Katerra Case Study SAP Data Hub and SAP BW/4HANA

5. The Changing Landscape of Data Warehousing

7. 7© 2017 SAP SE or an SAP affiliate company. All rights reserved. New Technologies and Expectations Disrupt Business-as-Usual Scientific Processing OLAP and OLTP Together Data Mining & Prediction Unstructured Data Planning Extensions Geospatial Processing Hierarchy Processing Graph Processing No SQL Processing Massive Scale Out Time Series Real Time, In-memory  Different Locations: Cloud, data lakes  Additional Types: Behavioral data, IoT  Higher Volumes: 40% growth YoY Predictive Streaming S/4HANA  Better Performance: Real-time results  Greater Scope: Predictive, agile analytics  Fast time-to-value

8. 8© 2017 SAP SE or an SAP affiliate company. All rights reserved. Key Business Challenges for the Digital Enterprise • Time and cost intensive • Discord between Business and IT • Process and data silos • Misaligned & competing KPIs • Antiquated UI and applications • Business drives Shadow IT Slow to deliver insights or deal with larger/faster data

9. 9© 2017 SAP SE or an SAP affiliate company. All rights reserved. Easier access to information for all users Reduce development efforts Simplicity Openness Modern Interface High Performance New UX for all users Leverage huge amounts of data in real time without compromise SAP BW/4HANA Design Principles and Values

10. SAP BW/4HANA Vision and Strategy

11. 11© 2017 SAP SE or an SAP affiliate company. All rights reserved. • Reduce Data Integration Cost • Reduce ETL Spend • Reduce On-prem Infrastructure Cost • Speed up change requests • Reduce Implementation Time • Reduce Development Spend Simplicity Openness Modern Interface High Performance • Improve Business User Productivity • Reduce Shadow Analytics • Improve User Adoption • Reduce Data Handling Wait Time • Improve decision- automation SAP BW/4HANA Design Principles and Values

12. 12© 2017 SAP SE or an SAP affiliate company. All rights reserved. Simplicity: Faster Response Through Enhanced Agility and Flexibility Simplified models • Number of Modeling (and DataSource) object types reduced from 10 to 4 • Field or InfoObject based Modeling • Support for external, structured and unstructured data Simplified data flows • Report at any layer with speed and flexibility • Virtually combine data across layers • Business and service level driven Simplified data tiering • Consistent approach for hot, warm and cold data • Allocate temperature by partition • Distribute data automatically between hot, warm and cold storage

13. 13© 2017 SAP SE or an SAP affiliate company. All rights reserved. Openness: Enabling Broader Business Insights Open to all data types via simplified data connectivity architecture • Dedicated Big Data Source System • Replicate data in real-time • Access data virtually • Load data using optimized processing Open to tight integration with SAP native DW approach  Integrated tools for managing mixed scenarios end-to-end  Leverage advantages of both approaches  Highly flexible implementation Open to flexible deployment  On-prem or cloud-based  SAP HANA Enterprise Cloud or AWS, Azure,  Highly flexible implementation

15. 15© 2017 SAP SE or an SAP affiliate company. All rights reserved. SAP BW/4HANA Content Difference to classic SAP BW content • Uses new SAP BW/4HANA features • Follows the LSA++ architecture • Delivered Content offers more flexibility in data acquisition and reporting • Makes use of the consolidated InfoObjects • Provides higher level of detail (line items, …) See public slide deck for latest updates Business Area

16. 16© 2017 SAP SE or an SAP affiliate company. All rights reserved. • No aggregates or roll-up processes • No performance specific objects • Fewer indices • Faster loading and processing • Significant performance gain through push-down of operations/calculations High Performance: Fast Insight to Action Data Modeling Process Orchestration SAP BW/4HANA OLAP Data Management Planning PushdownSAP HANA

17. 17© 2017 SAP SE or an SAP affiliate company. All rights reserved. SAP Big Data Warehouse Solution SAP HANA Platform Enterprise Edition Advanced Analytics – Spatial, Predictive, Text, Graph, Streaming, Series, etc. SAP BW/4HANA Streaming Analytics, SDI, Active-Active Business Content Virtualized LDM Customers’ Business Content Analytics, BI, Planning, Predictive All Sources ColumnarML EA Designer Consolidation Vora, IQ, DT NLS, Nodes, DWF Data Hub

18. 18© 2017 SAP SE or an SAP affiliate company. All rights reserved. SAP Vora and SAP BW/4HANA: Unique Big Data Integration BW/4HANA Transactional • Schema on write • Governed • High quality • Terra bytes Hadoop Data Lake • Schema on read • Largely ungoverned • Mixed quality • Peta bytes Copy? Copy? Virtual View SAP Vora Virtual View • Very fast OLAP against Hadoop • Connecting transactional data and Big Data in both directions • Graph, text, hierarchies for Big Data in memory • Runs inside Spark

19. 19© 2017 SAP SE or an SAP affiliate company. All rights reserved.  Natively integrate SAP S/4HANA embedded analytics models in SAP BW/4HANA  Best practice: The amount of data persisted in the Embedded BW should not exceed 20% of the overall data volume of the system SAP BW/4HANA Complements SAP S/4HANA SAP HANA PLATFORM SAP S/4HANA SAP Analytics Embedded Analytics Strategic Analytics SAP BW/4HANA  The Embedded BW in SAP S/4HANA is used to support certain business processes only, e.g. integrated financial planning processes (aka BPC Optimized)  Embedded BW is not recommended for building an Enterprise Data Warehouse, considering: – Upgrades and patches – Resource allocation and capacity planning – System Management, Workload Management and Monitoring – Backup & Recovery – Lifecycle management – High Availability & Disaster Recovery – Security – Production support systems ("Break fix systems") and Quality assurance system

20. 20© 2017 SAP SE or an SAP affiliate company. All rights reserved. New system BW on AnyDB BW/4HANA + BW/4HANA Starter Add-on New Installation System Consolidation BW on HANA BW on AnyDBBW on any DB Easy Conversion to SAP BW/4HANA BW on any DB BW on any DB Remote Conversion In-Place Conversion

22. #askSAP Q&A

23. Katerra Case Study Vamsi Paladugu Director, Data and Analytics, Katerra

24. #askSAP Q&A

25. SAP Data Hub & SAP BW/4HANA

26. 26© 2017 SAP SE or an SAP affiliate company. All rights reserved. Define data driven processes across complex enterprise landscapes • Access on-premises, cloud, or hybrid data sources –SAP or non-SAP (Amazon, Hadoop) • Leverage robust enterprise integration capabilities • Connect easily to SAP data management and application solutions as data sources • Connect to SAP and non-SAP applications and analytic solutions as endpoints SAP Data Hub Overview Security&Access SAP Data Hub SAP Data Hub Modeler Self Service Data Prep SAP Data Hub Cockpit Apps & Data Stores Analytics Data Science • Enterprise Apps: DWH, IoT, CRM, ERP, MDM • On-Prem data stores • Cloud/Hybrid stores • Dashboards • Ad Hoc Reporting • Self Services • ML / R / L / SPARK • Predictive Analytics • Advanced Big Data Analytics On-Premise Cloud Hybrid • SAP HANA & SAP BW • 3rd Party • Cloud object storage • Cloud Hadoop • Cloud/On Prem Hadoop Such as Such as Such as User Experience Data Discovery & Governance Data Pipelines & Orchestration Data Ingestion & On-Boarding Distributed Processing based on SAP VORA SAP HANA In-Memory, Modeling, Virtualization Hadoop, Object Storages Persistency's, Cluster Stores SAP Data Services, SAP LT Replication Server ETL, Batch, Data Integration, Replication Streaming & Ingestion KAFKA, Open Source Technologies Extensions& MicroServices Optimized Engines (Graph, Time Series…)

27. 27© 2017 SAP SE or an SAP affiliate company. All rights reserved. Enterprise Systems Data Discovery & Pipelines Orchestration & Monitoring Ingestion & Integration Hadoop Cloud Storages ML, Predictive,… Processing Runtime based on SAP Vora Distributed Data Systems SAP HANASAP BW/4HANA SAP S/4HANA Enterprise App SAP Data Hub Unifying Data Silos

28. 28© 2017 SAP SE or an SAP affiliate company. All rights reserved. Establish a Big Data Warehouse • Build a modern, open and hybrid DWH offering for any data • SAP BW/4HANA as modern and simplified core data warehouse solution • Implement and execute high volume transformations on Big Data Clusters Data Lake • Leverage Big Data landscapes for data onboarding and ingestion for various types of data and files • SAP Data Hub as orchestration and refinery application to address end to end processes Big Data (Data Lake, Data Swamp) High Volume Transformations SAP BW/4HANA Data Management Process Management OLAP Data Modeling SAP DATA HUB Data Ingestion & Onboarding ORCHESTRATION COCKPITPIPELINES DBs ERP, CRM… Massive Data Store O N-PREMISE | CLO UD | HYBRID SAP Data Hub and SAP BW/4HANA

29. 29© 2017 SAP SE or an SAP affiliate company. All rights reserved. Hadoop, S3, GCP, Azure SAP Data Hub – Runtime Component Overview SAP BW/4HANA SAP Data Hub application Kafka Files Data Pipelines & Flows SAP Vora Streams Videos Images CompositeProvider Advanced DSO S/4HANA RDBMS Structured Data OpenODS View SDA / SDI .csv .parquet Meta Data Repository Process Chains Meta Data (Hive, Atlas..) Meta Data Catalog Analytics Model Ingestion Big Data Processing Integration Refined Data SAP Data Services Data Flows

30. 30© 2017 SAP SE or an SAP affiliate company. All rights reserved. Data Warehousing with Social Media Hadoop (HDFS) SAP HANA SAPDataHub Console 3rdParty SAPPA/Spark Scala/Python SAPAnalyticsCloud STREAM COPY BATCH JOIN FILTER CLEANSE LOCK-UP SCRIPT MASK ANYMONIZE PARSE Store & Process LOAD EXTRACT FEDERATE TRANSFORM Master Data Master Data MODELING  Combine refined big data with enterprise data and corporate master data  Extract or federate data into SAP BW/4HANA  Ingest Data into S3 as Landing Zone for data  Orchestrate and schedule all related processes  Implement transformations and data pipelines  Harmonize data structures and look up of reference data  Execute operations on large data volumes SAP VORA Example Scenario SAPBW/4HANA OrchestrateAccess

31. #askSAP Q&A

32. Find Out More – Key Resources

33. 33© 2017 SAP SE or an SAP affiliate company. All rights reserved. Find out more about SAP BW/4HANA http://www.sap.com/products/bw4hana-data-warehousing.html Try SAP BW/4HANA https://www.sap.com/products/bw4hana-data-warehousing.trial.html Learn more with the SAP BW/4HANA Community https://www.sap.com/community/topic/bw4hana.html Take the OpenSAP MOOC dedicated to SAP BW/4HANA free of charge https://open.sap.com/courses/bw4h1 Read the Forrester Wave report ”Big Data Warehouse” https://reprints.forrester.com/#/assets/2/308/'RES136478'/reports Download the BARC Research report ”BI & Analytics with SAP BW/4HANA” https://www.sap.com/documents/2017/09/fa01b74e-d67c-0010-82c7-eda71af511fa.html Find Out More – Key Resources

34. 34© 2017 SAP SE or an SAP affiliate company. All rights reserved. Introducing the #askSAP Live Chat Series When: November 8, 2017, 8:00am Pacific Time Speakers: Byron Banks, VP of Product Marketing, SAP Timo Elliott, Global Innovation Evangelist, SAP When: January 10, 2017, 8:00am Pacific Time Speakers: Olivier Duvelleroy, General Manager of Enterprise BI, SAP Andreas Bitterer, Chief Analytics Evangelist EMEA, SAP When: December 14, 2017, 8:00am Pacific Time Speakers: Jayne Landry, VP of SAP Hybrid Analytics & SAP Leonardo Blair Wheadon, General Manager of Data Discovery, SAP www.facebook.com/sapanalytics

35. 35© 2017 SAP SE or an SAP affiliate company. All rights reserved.  New data reality force new thinking and acting  SAP BW/4HANA is THE innovation code line  Bridge between IT and the Business  Big Data scenarios with SAP Vora and SAP Data Hub  Natural companion to SAP S/4HANA  Versatile conversion paths from SAP BW 7.0 onwards  Strong market momentum 7 Key Points to Take Home

36. Thank you.