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James Watters Kafka Summit NYC 2019 Keynote

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Spring Boot and Kafka: The New Enterprise Platform

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James Watters Kafka Summit NYC 2019 Keynote

  1. 1. © Copyright 2019 Pivotal Software, Inc. All rights Reserved. James Watters April 2, 2019 Spring Boot+Kafka: the New Enterprise Platform
  2. 2. Our Journey: Bring continuous delivery with platform efficiency to enterprises.
  3. 3. New Projects Waterfall ITIL VMs JavaEE Batch Products Lean CI/CD Cloud Native Platforms Spring Boot Kafka Streaming Platform Old
  4. 4. “For tech to be a real driver of innovation and growth, IT needs to reorganize itself around flexible platforms.” —McKinsey
  5. 5. Progress so far...
  6. 6. “We are happy to announce that starting in 2018, Netflix is also making the transition to Spring Boot as our core Java framework, leveraging the community’s contributions via Spring Cloud Netflix.”
  7. 7. Platform at T-Mobile SPEED ➔ Time to Prod: 6 Months to weeks ➔ # of deployments: 10x increase SCALABILITY ➔ Avg: 300M Transactions a Day ➔ Peak: 1Billion Transactions a Day STABILITY ➔ 43% reduction in app response time ➔ 83% fewer incidents, fixed 67% faster
  8. 8. IBM IBM Monolith App Cache Win? App
  9. 9. Full transformation required a fundamental rethink….
  10. 10. “A business is a series of events and the reactions to those events.” —Jay Kreps, CEO, Confluent
  11. 11. “The only reason we don’t think of events….is that so far the technology has trained us to think of data as a static store.” —Neha Narkhede
  12. 12. We restarted with events first
  13. 13. What we’ve learned ➔ Events are the language bridge to the business ➔ This method of identifying bounded contexts is a secret to decoupled architecture ➔ “Tell don’t ask”
  14. 14. Completing the Journey IBM App Cache App Win? Microservices Event-driven Microservices AppAppApp AppAppApp AppAppApp
  15. 15. A platform built for a new way of thinking ➔ Event + Microservice first ➔ Team autonomy with platform efficiency ➔ Arbitrary scaling/scope across enterprise ➔ Turnkey multi-cloud
  16. 16. Streaming Data Platform
  17. 17. Immediate impacts
  18. 18. ➔ Global banking brand building greenfield core banking and payments with Spring Streams + Kafka ➔ Focus on microservices velocity required platform first thinking ➔ Kleppman like view of Kafka: “We count on Kafka for consistency, strict ordering, replay, durability and auditability.” ➔ Cross cloud replication A new continuously delivered banking platform
  19. 19. ➔ Turning moving packages into streaming data with RFID, Kafka and Spring Streams event based microservices ➔ Kafka, Kubernetes and Spring Boot in every shipping center ➔ Multiple business microservices teams can layer onto streaming platform to bin pack last mile services. ➔ Prepared for unanticipated uses cases Revolutionize our shipping efficiency with streaming microservices
  20. 20. PKS Managed Clusters Messaging Middleware Kafka Binder Spring Data Repository Event Driven Microservices LTL Quote Service Scan RFIC Services RFID Triggered Automation Services
  21. 21. ➔ 100,00+ container build out of Spring Streams, Kafka, key-value store ➔ Durability and consistency are critical for potential legal actions ➔ Multi-phase stream processing with Spring Streams leading to real-time microservices alerting analysts ➔ Cross-cloud replication based on Kafka ➔ Continuously delivery required for real time apps to improve accuracy and functionality as project expands Help secure a European country?
  22. 22. Receiver App process queue Fault tolerant receiver pairs staging and replication Apps Stream Workers Data Enrichment Stream Workers Data Enrichment Stream Workers Data Enrichment process queue Stream Workers Data Classification Stream Workers Data Classification Stream Workers Data Classification buffer queue S3 RAW Store Receiver App X.000 Channel Streams RDBMS Store 3 DC KAFKA Replication NoSQL Store Index Store
  23. 23. ➔ Mainframe and monolithic RDBMS data teams often the last to move to continuous delivery ➔ CDC, Event Shunting, patterns emerging allow streaming data platform teams to offer mainframe and legacy RDBMS events to microservices teams ➔ “AirBnB Pattern” growing across enterprises ➔ Each team can build appropriate persistence and achieve multi-DC replication with streaming platform Let’s empower pharmacy microservices developers while evolving our legacy?
  24. 24. PKS Foundation 1 PAS Foundation 1 Event Driven Microservice (Producers) Event Driven Microservice (Consumers) Domain Driven Microservice Legacy ODS ODS Kafka Cluster 1 Broker MirrorMaker cluster 1: topics cluster 1: topics PKS Foundation 2 PAS Foundation 2 Event Driven Microservice (Producers) Event Driven Microservice (Consumers) Domain Driven Microservice ODS Kafka Cluster 2 Broker MirrorMaker cluster 1: topics cluster 1: topics
  25. 25. Spring Kafka ++ We are all in… doubling down on a platform
  26. 26. Spring Cloud Stream deals with the Kafka scaffolding, so you don’t have to The power of Kafka streams for developers ➔ Rapid on-ramp for Kafka Streams consumption ➔ Simplifies construction of Event-Driven Stateful Microservices ➔ Focus on your processing logic not on configuration ➔ Full support of all Kafka streams functionality
  27. 27. We are all in… doubling down on a platform Spring Kafka ++ PCC Kafka Integration Pivotal Function Service (Knative ++ ) PAS Service Brokers (Custom today) PKS Confluent Operator Testing and Integration AppTX + Kafka CDC ++
  28. 28. Call to action ➔ Tim Berglund and Josh Long talk on Spring and Kafka ➔ Contribute to GitHub features and issues for Spring Stream ➔ Come visit us at the booth to talk streaming microservices
  29. 29. Deals with Configuration & Infrastructure Scaffolding Builds KStream and/or KTable, Binds Input Topics Assemble Stream Business Logic Binds Stream & Output Topic Spring Cloud Stream Developers Developers 💚Spring Cloud & Spring Cloud 💚 Kafka Deploy to Production on ANY cloud Spring Cloud Data Flow
  30. 30. Spring Framework Your Logic (in Spring Boot) Kafka (KStreams, KTables, etc) Spring for Apache Kafka Spring Cloud Stream
  31. 31. Spring Cloud Stream Application Kafka Binder Application Core Inputs Outputs
  32. 32. Receiver App process queue Fault tolerant receiver pairs staging and replication Apps Stream Workers Data Enrichment Stream Workers Data Enrichment Stream Workers Data Enrichment process queue Stream Workers Data Classification Stream Workers Data Classification Stream Workers Data Classification buffer queue S3 RAW Store Receiver App X.000 Channel Streams Use Case: European Security Agency Facts: ➔ Thousands of input streams ➔ No data loss solution ➔ 72h 50TB input buffer via Kafka ➔ 3 Kafka queue types 150TB per DC site ➔ Replication over 3 regions ➔ Non realtime clients for case management ➔ Realtime clients, enrichment and analytics RDBMS Store 3 DC KAFKA Replication NoSQL Store Index Store

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