SlideShare a Scribd company logo
Where we came from and where we’re going
March 2016
Snowplow was born in 2012
Web data: rich but GA /
SiteCatalyst are limited
“Big data” tech
• Marketing, not product
analytics
• Silo’d: can’t join with
other customer data
Snowplow
• Open source
frameworks
• Cloud services
• Open source click
stream data warehouse
• Event level: any query
• Built on top of
Cloudfront / EMR /
Hadoop
The plan: spend 6 months
building a pipeline…
…then get back to using the data
So what went wrong?
Increased project scope
• Click stream data warehouse -> Event analytics
platform
• Collect events from anywhere, not just the web
• Make event data actionable in real-time
• Support more in-pipeline processing steps (enrichment
and modeling)
• Support more storage targets (where your data is has big
implications for what you can do with that data)
Track events from anywhere
• Events
• Entities
Make event data actionable in
real-time
• Personalization
• Marketing automation
• Content analytics
Today, Snowplow is an event
data pipeline
What makes Snowplow special?
• Data pipeline evolves with your business
• Channel coverage
• Flexibility: where your data is delivered
• Flexibility: how your data is processed
(enrichment and modeling)
• Data quality
• Speed
• Transparency
Used by 100s (1000s?) of companies…
…to answer their most important business questions
But there’s still much more to
build!
• Improve automation around schema evolution
• Make modeling event data easier, more robust,
more performant
• Support more storage targets
• Make it easier to act on event data
Data modeling in Spark
Druid, BigQuery, graph databases
Analytics SDKs, Sauna
Iglu: machine-readable schema registry
Questions?
• Can take questions now or after the other talks

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Snowplow: where we came from and where we are going - March 2016

  • 1. Where we came from and where we’re going March 2016
  • 2. Snowplow was born in 2012 Web data: rich but GA / SiteCatalyst are limited “Big data” tech • Marketing, not product analytics • Silo’d: can’t join with other customer data Snowplow • Open source frameworks • Cloud services • Open source click stream data warehouse • Event level: any query • Built on top of Cloudfront / EMR / Hadoop
  • 3. The plan: spend 6 months building a pipeline… …then get back to using the data
  • 4. So what went wrong?
  • 5. Increased project scope • Click stream data warehouse -> Event analytics platform • Collect events from anywhere, not just the web • Make event data actionable in real-time • Support more in-pipeline processing steps (enrichment and modeling) • Support more storage targets (where your data is has big implications for what you can do with that data)
  • 6. Track events from anywhere • Events • Entities
  • 7. Make event data actionable in real-time • Personalization • Marketing automation • Content analytics
  • 8. Today, Snowplow is an event data pipeline
  • 9. What makes Snowplow special? • Data pipeline evolves with your business • Channel coverage • Flexibility: where your data is delivered • Flexibility: how your data is processed (enrichment and modeling) • Data quality • Speed • Transparency
  • 10. Used by 100s (1000s?) of companies… …to answer their most important business questions
  • 11. But there’s still much more to build! • Improve automation around schema evolution • Make modeling event data easier, more robust, more performant • Support more storage targets • Make it easier to act on event data Data modeling in Spark Druid, BigQuery, graph databases Analytics SDKs, Sauna Iglu: machine-readable schema registry
  • 12. Questions? • Can take questions now or after the other talks