A look back at the history of the Snowplow platform, and a look forward at the key areas of product development on our roadmap

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