Building a Real-Time Forecasting Engine with Scala and Akka
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In this presentation, Steven Laan, Product Owner and Advanced Real-Time Analytics Dev Engineer, ING Group talks about the Why, What, and How of real time transaction forecasting. Topics include: visual end product, architecture landscape, actor system solution and a bit of ING Way of Working.
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Building a Real-Time Forecasting Engine with Scala and Akka
- 1. Kijk vooruit! Steven Laan Building a real-time forecasting engine with Scala and Akka Lightbend CTO Roundtable 24-01-2017
- 2. Product Owner at ING Analytics Academic background: Artificial Intelligence Working as Dev Engineer since 2014 Steven.Laan@ing.nl https://nl.linkedin.com/in/steven-laan-64222b48 Steven Laan
- 3. Agenda 3 Why ING is a data-driven enterprise1. Strategy How RAF is build3. Architecture and Technology How we implement the tools4. Way of Working The coolest feature in ING’s mobile app2. Use case: RAF Lessons learned and the road ahead5. Conclusions
- 5. ING’s Think Forward strategy was launched in March 2014 5
- 6. We are committed to helping our customers stay a step ahead by making their data work harder for them 6 Compliant Omnichannel Predictive Secure & reliable Personal
- 7. How do we want to use data at ING? 7 the right message at the right time in the right channel to the right customer Message Time Channel What does ‘right’ mean? • We want to be as relevant as possible • ‘right’ = interesting enough to react positively (i.e. to click)
- 9. What? • Provide customers insight in their future transactions How? • Mobile app feature • Start small: MVP • Agile: Keep improving Real-time Account Forecasting 9
- 10. Mobile interface 10 Main screen Transaction detailsTerms & conditions Info page
- 13. • Called by Mobile Platform (REST) • Calls backend services (multiple protocols) • Returns future transactions (JSON) • Certain transactions • Predicted transactions Forecasting API 13
- 14. • Scala • Akka • Git • Sbt • Nolio • Jenkins • Graphite / Grafana • Spark Used Technologies 14
- 15. • Scala • Interop with JVM • Functional • Akka • Scalability • Domain Why Scala / Akka 15
- 16. Flow of a request 16 Spray HTTP Service RequestActor FutureActors HistoryActors Historic transactions services Planned transactions services ForecastActor Forecasting Library
- 17. Flow of a request 17 Spray HTTP Service RequestActor FutureActors HistoryActors Historic transactions services Planned transactions services ForecastActor Forecasting Library
- 18. Flow of a request 18 Spray HTTP Service RequestActor FutureActors HistoryActors Historic transactions services Planned transactions services ForecastActor Forecasting Library
- 19. • RequestActor per request • Scalable • Asynchronous • Dedicated service actors • Configurability • Error handling • Separate forecasting library • Separation of concern • Reusable • Parallel development Actor System Architecture 19
- 20. Dealing with slower processes 20 Real time transactions service Slow (legacy) system Forecasting Library Forecasting APIFront End DB Preprocessing
- 21. • Move towards streaming solutions • Fast data • Kafka / Flume / Flink • Improve forecasting library Road ahead 21
- 22. HOW: Way of working
- 23. ING is an Experimental Enterprise Source: Edd Dumbill, Forbes, 2014
- 24. Squad Tribes, squads, chapters, guilds, … SquadSquad Chapter Guild DS DA OpsOps CJE DS DA DS CJE DS Ops DS CJE Dev DevDevDevDev
- 25. A fun place to work! 25
- 26. 26 Follow us to stay a step ahead ING.com YouTube.com/ING SlideShare.net/ING@ING_News LinkedIn.com/company/ING Flickr.com/INGGroupFacebook.com/ING