Snowplow and Kinesis - Presentation to the inaugural Amazon Kinesis London User Group
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This is my presentation to the inaugural meetup of the Amazon Kinesis London User Group. In it I briefly introduced Snowplow, explained why we were excited about Kinesis (drawing on my "three eras" blog post) and then set out how we are updating Snowplow to run on Kinesis. I concluded with a live demo of what we have running on Kinesis so far.
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Snowplow and Kinesis - Presentation to the inaugural Amazon Kinesis London User Group
- 1. Let’s introduce Amazon Kinesis Inaugural meetup of the Amazon Kinesis - London User Group
- 2. This evening • Introducing Amazon Kinesis, Ian Meyers, AWS • Pizza and drinks break • Kinesis and Snowplow, Alex Dean, Snowplow Analytics • Drinks • All courtesy of our hosts:
- 4. Snowplow and Kinesis 1. Snowplow – who we are 2. Why are we excited about Kinesis? 3. Adding Kinesis support to Snowplow 4. Live demo! 5. Questions
- 5. Snowplow – who we are
- 6. Today, Snowplow is primarily an open source web analytics platform Snowplow: data pipeline Website / webapp Amazon S3 Collect Transform and enrich Amazon Redshift / PostgreSQL • Your granular, event-level and customer-level data, in your own data warehouse • Connect any analytics tool to your data • Join your web analytics data with any other data set
- 7. Snowplow was born out of our frustration with traditional web analytics tools… • Limited set of reports that don’t answer business questions • • • • Traffic levels by source Conversion levels Bounce rates Pages / visit • Web analytics tools don’t understand the entities that matter to business • Customers, intentions, behaviours, articles, videos, authors, subjects, services… • …vs pages, conversions, goals, clicks, transactions • Web analytics tools are siloed • Hard to integrate with other data sets incl. digital (marketing spend, ad server data), customer data (CRM), financial data (cost of goods, customer lifetime value)
- 8. …and out of the opportunities to tame big data new technologies presented These tools make it possible to capture, transform, store and analyse all your granular, event-level data, to you can perform any analysis
- 9. Snowplow is composed of a set of loosely coupled subsystems, architected to be robust and scalable 1. Trackers A 2. Collectors B 3. Enrich C 4. Storage D 5. Analytics Generate event data Receive data from trackers and log it to S3 Clean and enrich raw data Store data ready for analysis Examples: • Javascript tracker • Python / Lua / No-JS / Arduino tracker Examples: • Cloudfront collector • Clojure collector for Amazon EB Built on Scalding / Cascading / Hadoop and powered by Amazon EMR Examples: • Amazon Redshift • PostgreSQL • Amazon S3 • Batch-based A D Standardised data protocols • Normally run overnight; sometimes every 4-6 hours
- 10. Why are we excited about Kinesis?
- 11. A quick history lesson: the three eras of business data processing 1. The classic era, 1996+ 2. The hybrid era, 2005+ 3. The unified era, 2013+ For more see http://snowplowanalytics.com/blog/2014/01/20/the-three-eras-of-business-data-processing/
- 12. The classic era, 1996+ OWN DATA CENTER NARROW DATA SILOES LOW LATENCY LOCAL LOOPS Point-to-point connections CMS E-comm Local loop ERP Local loop Silo CRM Local loop Silo Local loop Silo Nightly batch ETL process HIGH LATENCY WIDE DATA COVERAGE Management reporting Data warehouse FULL DATA HISTORY Silo
- 13. The hybrid era, 2005+ CLOUD VENDOR / OWN DATA CENTER NARROW DATA SILOES Search LOW LATENCY LOCAL LOOPS CMS Local loop SAAS VENDOR #1 E-comm Local loop Silo Local loop Silo APIs ERP Local loop Silo CRM Local loop Silo Bulk exports SAAS VENDOR #2 Stream processing Micro-batch processing Batch processing Batch processing Email marketing Local loop Product rec’s Local loop LOW LATENCY Systems monitoring Data warehouse Hadoop SAAS VENDOR #3 Local loop LOW LATENCY Management reporting HIGH LATENCY Ad hoc analytics HIGH LATENCY Web analytics Local loop
- 14. The unified era, 2013+ CLOUD VENDOR / OWN DATA CENTER NARROW DATA SILOES SOME LOW LATENCY LOCAL LOOPS Search CMS Silo E-comm Silo APIs ERP Silo LOW LATENCY Streaming APIs / web hooks WIDE DATA SAAS VENDOR #2 COVERAGE Unified log Email marketing FEW DAYS’ DATA HISTORY Hadoop HIGH LATENCY < WIDE DATA COVERAGE > < FULL DATA HISTORY > CRM Silo Eventstream Archiving SAAS VENDOR #1 Ad hoc analytics Product rec’s Systems monitoring Management reporting Fraud detection Churn prevention LOW LATENCY
- 15. The unified log is Kinesis (or Kafka) CLOUD VENDOR / OWN DATA CENTER NARROW DATA SILOES Search SAAS VENDOR #1 SOME LOW LATENCY LOCAL LOOPS CMS Silo E-comm Silo APIs ERP Silo CRM Silo Streaming APIs / web hooks Eventstream SAAS VENDOR #2 Unified log Archiving Hadoop HIGH LATENCY < WIDE DATA COVERAGE > < FULL DATA HISTORY > Email marketing Ad hoc analytics Product rec’s Systems monitoring Management reporting Fraud detection Churn prevention LOW LATENCY
- 16. Can we implement Snowplow on top of Kinesis? CLOUD VENDOR / OWN DATA CENTER NARROW DATA SILOES Search SAAS VENDOR #1 SOME LOW LATENCY LOCAL LOOPS CMS Silo E-comm Silo APIs ERP Silo CRM Silo Streaming APIs / web hooks Eventstream SAAS VENDOR #2 Unified log Archiving Hadoop HIGH LATENCY < WIDE DATA COVERAGE > < FULL DATA HISTORY > Email marketing Ad hoc analytics Product rec’s Systems monitoring Management reporting Fraud detection Churn prevention LOW LATENCY
- 17. Adding Kinesis support to Snowplow
- 18. Where we are heading with our Kinesis architecture Snowplow Trackers Scala Stream Collector Raw event stream S3 sink Kinesis app S3 Enrich Kinesis app Enriched event stream Redshift sink Kinesis app Redshift Bad raw events stream
- 19. We took an important first step in our last release… 0.8.12 pre-0.8.12 hadoop-etl scala-hadoopenrich scala-kinesis-enrich Record-level enrichment functionality scala-common-enrich
- 20. … and the next release should get us much closer Snowplow Trackers Scala Stream Collector Raw event stream S3 sink Kinesis app S3 Enrich Kinesis app Enriched event stream Redshift sink Kinesis app Redshift Bad raw events stream
- 21. Live demo!
- 23. And finally… Huge thanks to our hosts!