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WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
WHY SAP Real Time Data Platform - RTDP
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WHY SAP Real Time Data Platform - RTDP

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WHY SAP Real Time Data Platform - RTDP

WHY SAP Real Time Data Platform - RTDP

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  • 1. Big Data in Real-Time Uğur CANDAN SAP Turkey - Chief Operating Officer @ugurcandan ugurcandan.net
  • 2. Youtube in-memory database © 2011 SAP AG. All rights reserved. 2
  • 3. © 2011 SAP AG. All rights reserved. 3
  • 4. © 2011 SAP AG. All rights reserved. 4
  • 5. © 2011 SAP AG. All rights reserved. 5
  • 6. © 2011 SAP AG. All rights reserved. 6
  • 7. © 2011 SAP AG. All rights reserved. 7
  • 8. © 2011 SAP AG. All rights reserved. 8
  • 9. © 2011 SAP AG. All rights reserved. 9
  • 10. © 2011 SAP AG. All rights reserved. 10
  • 11. © 2011 SAP AG. All rights reserved. 11
  • 12. © 2011 SAP AG. All rights reserved. 12
  • 13. © 2011 SAP AG. All rights reserved. 13
  • 14. © 2011 SAP AG. All rights reserved. 14
  • 15. © 2011 SAP AG. All rights reserved. 15
  • 16. © 2011 SAP AG. All rights reserved. 16
  • 17. © 2011 SAP AG. All rights reserved. 17
  • 18. © 2011 SAP AG. All rights reserved. 18
  • 19. © 2011 SAP AG. All rights reserved. 19
  • 20. © 2011 SAP AG. All rights reserved. 20
  • 21. © 2011 SAP AG. All rights reserved. 21
  • 22. Technology today requires tradeoff A breakthrough in today’s information processing architecture is needed DEEP Complex & interactive questions on granular data OR HIGH SPEED Fast response-time, interactivity DEEP Complex & interactive questions on granular data HIGH SPEED BROAD Fast response-time, interactivity Big data, many data types SIMPLE No data preparation, no pre-aggregates, no tuning © 2011 SAP AG. All rights reserved. REAL -TIME Recent data, preferably realtime SIMPLE No data preparation, no pre-aggregates, no tuning 22
  • 23. SAP HANA Platform – More than just a database Any Apps SAP Business Suite Any App Server Supports any Device and BW ABAP App Server SQL MDX R JSON Open Connectivity SAP HANA Platform SQL, SQLScript, JavaScript Spatial Search Text Mining Stored Procedure & Data Models Application & UI Services Business Function Library Predictive Analysis Library Database Services Planning Engine Rules Engine Integration Services Transaction Unstructured Machine HADOOP Real-time Locations Other Apps SAP HANA Platform Converges Database, Data Processing and Application Platform Capabilities & Provides Libraries for Predictive, Planning, Text, Spatial, and Business Analytics to enable business to operate in real-time. © 2011 SAP AG. All rights reserved. 23
  • 24. Dünyanın en büyük in-memory veritabanı sistemi – Santa Clara, CA 250 HANA sunucusu | 250TB Ana Bellek | 10,000 x86 Core © 2011 SAP AG. All rights reserved. 24
  • 25. Breakthrough solutions from startups & ISVs A single platform powering next generation of applications nexvisionix DRIVING ADOPTION RECENT PROJECTS  Platform to imagine new generation of applications  Industry solutions - Healthcare, Capital Markets  Simple consumption model – lowering barriers to entry  Consumer and enterprise applications  Rapid commercialization of innovation  www.startups.saphana.com (700+ Startups & ISVs) © 2011 SAP AG. All rights reserved. 25
  • 26. Predictive Analytics & Machine Learning Transforming the Future with Insight Today Hadoop/ Sybase IQ, Sybase ASE, Teradata SAP HANA KNN classification Regression Main Memory C4.5 decision tree K-means Virtual Tables SQL Script Optimized Query Plan Spatial, Machine, Text Analysis Real-time data PAL R-scripts ABC classification Weighted score tables Associate analysis: market basket R-Engine Spatial Data Unstructured HANA Studio/AFM, Apps & Tools Accelerate predictive analysis and scoring with in-database algorithms delivered out-of-the-box. Adapt the models frequently © 2011 SAP AG. All rights reserved. Execute R commands as part of overall query plan by transferring intermediate DB tables directly to R as vector-oriented data structures Predictive analytics across multiple data types and sources. (e.g.: Unstructured Text, Geospatial, Hadoop) 26
  • 27. Innovation Previously Infeasible Predict and analyzes game player behavior in real-time Real-time insights, analysis, and consumer engagement for increased revenue and decreased churn © 2011 SAP AG. All rights reserved. 27
  • 28. Simplicity Previously Unachievable eBay Early Signal Detection System powered by Predictive Analytics Automated signal detection system to proactively respond to real-time market dynamics © 2011 SAP AG. All rights reserved. 28
  • 29. Product: Agile Datamart Yodobashi - POS Data Analizi Business Challenges 250 million POS  Lack of real-time insights into POS data make it difficult to create effective, tailored sales promotions and marketing campaigns sales order line items  Need shorter response time for customer segmentation to plan sales campaigns 10-12 minute Technical Challenges sales campaign planning (not possible before)  Inability to process big data (billions) POS records quickly because of high latency and static reporting  Shop floor staff not able to access relevant information on-the-fly, with iPad Benefits 100,000x faster sales analysis – from 3 days to 2-3 seconds © 2011 SAP AG. All rights reserved.  Real-time insights into POS data improve customer satisfaction and merchandising  Dynamic personalized offerings while customer is at store or on web site 29
  • 30. 12,000 Staff with 3,200 pure scientist, 650,000 patients/year, 1,4 B€ revenue 500,000 data points from each cancer patient. Instant patient data analysis during treatment
  • 31. Mitsui Knowledge Industry Healthcare industry – Cancer cell genomic analysis 408,000x faster than traditional diskbased systems in technical PoC 216x faster DNA analysis results from 2,5 days to 20 minutes © 2011 SAP AG. All rights reserved. 31
  • 32. Thank you

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