Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.

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

1.359 Aufrufe

Veröffentlicht am

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

Veröffentlicht in: Daten & Analysen
  • Loggen Sie sich ein, um Kommentare anzuzeigen.

  • Gehören Sie zu den Ersten, denen das gefällt!

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

  1. 1. Where we came from and where we’re going March 2016
  2. 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. 3. The plan: spend 6 months building a pipeline… …then get back to using the data
  4. 4. So what went wrong?
  5. 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. 6. Track events from anywhere • Events • Entities
  7. 7. Make event data actionable in real-time • Personalization • Marketing automation • Content analytics
  8. 8. Today, Snowplow is an event data pipeline
  9. 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. 10. Used by 100s (1000s?) of companies… …to answer their most important business questions
  11. 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. 12. Questions? • Can take questions now or after the other talks