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IS OLAP DEAD IN THE AGE OF BIG DATA?

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Hadoop Summit 2015

Veröffentlicht in: Technologie
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IS OLAP DEAD IN THE AGE OF BIG DATA?

  1. 1. IS OLAP DEAD IN THE AGE OF BIG DATA? AJAY ANAND, VP Products, Kyvos Insights Inc. Yogesh Joshi, Head of Big Data and Analytics, AIG
  2. 2. AGENDA •  Why OLAP? •  Common implementation scenarios with Hadoop •  Issues with traditional tools connecting to Big Data •  OLAP on Hadoop: ROLAP, HOLAP, in-memory MOLAP, distributed MOLAP on disk •  The Kyvos approach •  Use Cases •  Using OLAP for Risk Analysis •  Q&A
  3. 3. WHY OLAP? •  What OLAP can provide: •  Fast, interactive insights •  Ad hoc analysis •  Visual exploration, slice and dice, drill down •  Multi-dimensional view of data •  BUT, traditional OLAP solutions struggle with •  Massive increase in business data volume •  Explosion of cardinality (granularity) and dimensions •  Variety of data sources
  4. 4. WHAT DO USERS WANT? •  Self Service, Interactive Analytics •  On Big Data •  At any scale, with all kinds of datasets •  With instant response times, no waiting
  5. 5. BIG DATA SCENARIO HADOOP Cost effective Scalable Flexible Hard to use Not interactive Transactional Data Customer Interaction Internet of Things Web Interaction Logs Product Usage Logs Social Media Interactions System Logs Sales, Inventory, Revenue Audit logs Operational Data Security Logs Issues: Accessibility, Ease of use, Support for high performance, interactive analytics Business Analyst
  6. 6. COMMON BIG DATA SCENARIO Data Mart / EDW OLAP CubeHADOOP Analytics / Visualization Tool Transform and Process on Hadoop, Load into DBMS •  Transactional data •  Clickstream data •  Log files •  Other structured and unstructured data
  7. 7. ISSUES Data Warehouse / DBMS OLAP Cube HADOOP Analytics / Visualization Tool Transform and Process on Hadoop, Load into DBMS •  Transactional data •  Clickstream data •  Log files •  Other structured and unstructured data SCALABILITY, PERFORMANCE LIMITATIONS LATENCY
  8. 8. OLAP ON HADOOP •  Approaches •  ROLAP / HOLAP on Hive / Impala •  In-memory MOLAP •  MOLAP on Hadoop
  9. 9. KYVOS SOLUTION: MOLAP ON HADOOP KYVOS •  Dashboards •  Interactive visualizations •  Explore, slice and dice, drill down LOAD HADOOP CUBES ON HADOOP TRANSFORM •  Transactional data •  Clickstream data •  Log files •  Other structured and unstructured data
  10. 10. TRANSFORMATIONS WITHOUT CODING
  11. 11. FLEXIBLE CUBE DESIGN
  12. 12. INTERACTIVE VISUALIZATIONS
  13. 13. SCALABLE ARCHITECTURE
  14. 14. USE CASES • Entravision: • Consumer behavior analysis for Latino market • Moving from sample and survey data (28K self reported diaries) to empirical measurements on 15M+ adults • Drill down to lowest levels of granularity • Transactional data, purchase behavior, household characteristics, consumer characteristics • Precise measurability to prove efficiencies and ROI for media planning
  15. 15. USE CASES •  Telecom subscriber profiling •  20M subscribers •  500B+ rows of data •  60 days usage •  96 node cluster •  Hourly incremental builds
  16. 16. USE CASES •  Risk analysis for financial services / insurance •  Operational analytics •  Web analytics for online shopping in the travel industry •  Set top data analytics
  17. 17. RISK ANALYSIS •  Yogesh Joshi, Head of Big Data and Analytics, AIG
  18. 18. Q&A •  Join us at Booth S5 •  www.kyvosinsights.com

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