Getting Started with Big Data Analytics
-
Upload
rob-winters -
Category
Technology
-
view
393 -
download
1
description
Transcript of Getting Started with Big Data Analytics
{The Single Step
Beginning your big data journey
How is “big data” different from traditional BI?
What components do we need for big data?
What magic can we work with big data?
Today’s Stops
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
VELOCITY
VARIETY
VERACITY
What is big data?
VALUEThe Only V that Matters
Traditional BI: Know before you measure
X Matters
Define Metrics
Define Requirement
s
Develop Data Source
Design Data Mart
Design Report
Sign Off Report
Reporting Available
SlowIT-CentricInflexible
Big Data BI: Agile approach, data first
Capture
Explore Define
Apply +
Track
OpenAdaptive
Evolving Structure
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
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 XGroup 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
Spil: Harmony of open source/commercialUnstructured 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
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 FlexibleBe FastMake MistakesFind Value
A tool, not a goal
Good Luck on your Journey!
Rob WintersDirector, Reporting/AnalyticsSpil Gameswww.robertdwinters.com