Getting Started with Big Data Analytics


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Presentation at IT Tinget 2013 on what is big data, agile BI, and technology considerations when starting a Big Data program

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Getting Started with Big Data Analytics

  1. 1. { The Single Step Beginning your big data journey
  2. 2. Today’s Stops
  3. 3. Spil Games: A leader in online gaming • 180 million monthly and 12 million daily players • More than one billion gameplays monthly • >50 websites, local in 15 languages • Active in every country of the world (even Vatican City!) • Platform, Publisher, Developer
  4. 4. What is big data?
  5. 5. X Matters Define Metrics Define Requirements Develop Data Source Design Data Mart Design Report Sign Off Report Reporting Available Slow IT-Centric Inflexible
  6. 6. Big Data BI: Agile approach, data first Capture Explore Define Apply + Track Open Adaptive Evolving Structure
  7. 7. Do we need real time analytics? Traditional ETLReal Time • Once a day • Once a week • Delayed • Faster than human perception • <200 milliseconds “In Time” In Time: Information is available fast enough to influence decisions • Following a product release (hours) • While a customer is in the shop/on the site (minutes) • While the query runs (seconds) The Velocity Continuum In Time: Fast enough, Cheap enough, Easy enough
  8. 8. Parts and needs of a big data stack Unstructured data intake Unstructured data storage Structured data storage Human interface layer Predictive analytics tools Select A,B,sum(C) From X Group by 1,2 • High Query Performance • Denormalized • Scalable; high concurrency • Cheap • Flexible Schema • Easy Management • Scalable • Schemaless or adaptive schema • Resilient • Highly Flexible • Simple to use • In-tool metadata • Not memory constrained • Flexible inputs/outputs • Easy iteration
  9. 9. Spil: Harmony of open source/commercial Unstructured data intake Unstructured data storage Structured data storage Human interface layer Predictive analytics tools • >100x faster than based systems • Handles tables >10B rows easily • Excellent concurrency on load/query • Data marts not required • Cross-platform merging • Anyone can develop • Open source • Easy development • Integrates with rest of tools • Industry standard • Open source • Ecosystem • Existing infrastructure • Integration with production systems
  10. 10. Demographic Prediction Analytical use cases Multivariate Testing/Site Optimization Explore, Learn, Predict, Measure
  11. 11. Getting your big data off the ground Start Fresh Have a Problem Be Agile Pragmatism > Perfection Be Flexible Be Fast Make Mistakes Find Value A tool, not a goal
  12. 12. Good Luck on your Journey! Rob Winters Director, Reporting/Analytics Spil Games