{
The Single Step
Beginning your big data journey
Today’s Stops
Spil Games: A leader in online gaming
• 180 million monthly and 12 million daily players
• More than one billion gameplays...
What is big data?
X Matters
Define Metrics
Define
Requirements
Develop Data
Source
Design Data
Mart
Design Report
Sign Off
Report
Reporting
...
Big Data BI: Agile approach, data first
Capture
Explore Define
Apply +
Track
Open
Adaptive
Evolving Structure
Do we need real time analytics?
Traditional ETLReal Time
• Once a day
• Once a week
• Delayed
• Faster than human
percepti...
Parts and needs of a big data stack
Unstructured
data intake
Unstructured
data storage
Structured
data storage
Human
inter...
Spil: Harmony of open source/commercial
Unstructured
data intake
Unstructured
data storage
Structured
data storage
Human
i...
Demographic Prediction
Analytical use cases
Multivariate Testing/Site Optimization
Explore, Learn, Predict, Measure
Getting your big data off the ground
Start Fresh
Have a Problem
Be Agile
Pragmatism >
Perfection
Be Flexible
Be Fast
Make ...
Good Luck on your
Journey!
Rob Winters
Director, Reporting/Analytics
Spil Games
www.robertdwinters.com
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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 www.robertdwinters.com
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