Data Management in SAP Environments

Transcription

1 Data Management in SAP Environments the Big Data Impact Berlin, June 2012 Dr. Wolfgang Martin Analyst, ibond Partner und Ventana Research Advisor

2 Data Management in SAP Environments Big Data What it is and why we have to deal with it In-Memory Data Management SAP s Answer to Big Data Big Data Trends and Challenges S.A.R.L. Martin

3 Big Data The Definition Big Data is defined by three properties: Volume, Variety, Velocity, plus an increasing need for analysis. 90% of Big Data is unstructured. Big Data is beyond the Web and Social Media. Big Data is not really new S.A.R.L. Martin

4 Big Data Benefits and Profits The 5 Key Types of Advantages*: Transparency through Big Data, Testing of Decisions, Individualization in Real-Time, Intelligent Process Control and Automation, Innovative Data-Driven Business Models. * after McKinsey S.A.R.L. Martin

5 Big Data Market Development Market Forecast EMC estimates $70b in Investments in Hadoop: $350m. Big Data is more than a hype. SAP s Big Data revenue in 2011 is estimated at $85m. after Wikibon S.A.R.L. Martin

6 Big Data Strategies Agile Big Data How can we take the idea of spreadsheets to big data? Operational Big Data How can we create an infrastructure so everyone can get the benefit of what we learn from big data? High Resolution Management How can we change the way we manage our businesses based on the high resolution view big data provides? The SAP Strategy for Big Data: SAP is attempting to create an integrated approach that allows companies to perform analytics, make big data operational, and support applications for high resolution management all in one environment. Sanjay Poonen, President and corporate officer of SAP Global Solutions S.A.R.L. Martin

7 Big Data Technologies Big Data Analytics Data Discovery Textanalytics Location Intelligence Big Data Management Big Data Integration Big Data Repository Big Data Quality Big Data Databases Analytical Data Bases NoSQL Data Bases Hadoop Storage S.A.R.L. Martin

8 Big Data Architecture for Analytics Social Media Localisation Data Call Data Records RFID Events / Sensors Operational Data Files, XML, Spreadsheets Data Warehouse Machine Data Data Discovery Text Analytics Data/Text Mining Search Enrichment Big Data Management Big Data Analytics NoSQL (Not onlysql) and analytical Data Bases / Hadoop S.A.R.L. Martin

9 Data Management in SAP Environments Big Data What it is and why we have to deal with In-Memory Data Management SAP s Answer to Big Data Big Data Trends and Challenges S.A.R.L. Martin

10 In-Memory Data Management: Definition SAP HANA Design Principles: Data Centric Architecture. It is based on more than just in-memory. It runs transactional and analytical applications. Enterprise Performance In-Memory Circle (EPIC) after Dr. A. Zeier, Hasso Plattner Institut S.A.R.L. Martin

11 In-Memory as Data Appliance (I) Innovations in HW Technology Multi-core architecture, Massive parallel scaling across many blades, One blade = one enterprise class server, 64bit address space enables 2TB, 100GB data throughput, Decline in cost-performance ratio S.A.R.L. Martin

12 In-Memory as Data Appliance (II) Innovations in SW Technology Row and column stores, Compression, Partitioning, No aggregate tables, Insert only. In-memory data management enables innovative applications. Transaction and analytics in one application. Key improvement is to bring application logic closer to the storage layer. Stored procedures and business library functions S.A.R.L. Martin

13 Data Management in SAP Environments Big Data What it is and why we have to deal with In-Memory Data Management SAP s Answer to Big Data Big Data Trends and Challenges S.A.R.L. Martin

14 The Open Source Answer: Hadoop Apache Software Foundation project consisting of three components: Storage layer HDFS (Hadoop Distributed File System), 64MB data blocks, redundancy, no insert. The MapReduce programming framework for parallel processing of queries (proposed by Google). A function library. Add ons: HLQL (high level query languages) Hive, Pig and JAQL. ZooKeeper (Managing distributed configurations). Chukwa (Real-Time Monitoring) et al. Downside: Batch-oriented, no ACID transactions. Note: Lack of experience, of resources, of reliability S.A.R.L. Martin

15 Big Data Challenges Identifying talented people that know Big Data and analytics, and have made their experiences. Selecting the right technology and tools (but first define your Big Data strategy!). Measuring relevance of information for a given problem. Thinking out-of-the-box always and again. Finding an end and trusting the results of the analyses S.A.R.L. Martin

16 Big Data Enterprises should address Big Data: Big Data is no Hype, it will even change the way we manage enterprises. Big Data can improve competitiveness and create innovation. Example: Big Data transforms customer knowledge into market knowledge. The challenges: set up a strategy, change the org and get the necessary skilled resources. Note: Big Data Technology is still young and immature. For more information, please see Contact: info@wolfgang-martin-team.net S.A.R.L. Martin