TEC-3617 Zero to Data Streaming Platform on Confluent Cloud

As presented at IBM TechXChange 2026 in Atlanta, Georgia

https://bit.ly/4suzArZ

Transcript

1. Zero to Data Streaming Platform on Confluent Cloud TEC-3617 Zero to Data Streaming Platform on Confluent Cloud Sandon Jacobs IBM, Senior Developer Advocate Atlanta, GA October 26–29
2. Who am I?  Developer Advocate  Raleigh, North Carolina  Build it on the JVM!  Terraform all the things!  Deploy it to prod!  But test the heck out of it… IBM TechXchange | © 2026 IBM Corporation 2
3. Zero to Data E S U O Streaming H M A E STR Platform on Confluent Cloud TEC-3617 Zero to Data Streaming Platform on Confluent Cloud Sandon Jacobs IBM, Senior Developer Advocate Atlanta, GA October 26–29
4. What is a “DSP,” anyway? Stream data with Apache Kafka IBM TechXchange | © 2026 IBM Corporation Connect data sources and destinations with Kafka Connect Process streams with Apache Flink Govern data quality, schemas, lineage, and access 4
5. Stream IBM TechXchange | © 2026 IBM Corporation 5
6. Just getting here, you interacted with a data stream Ordering a coffee Hailing a ride share Check into your hotel Register for the conference IBM TechXchange | © 2026 IBM Corporation 6
7. IBM TechXchange | © 2026 IBM Corporation 7
8. Ⓡ Apache Kafka – the data substrate High-throughput Scalability Durable Storage Highly available IBM TechXchange | © 2026 IBM Corporation 8
9. Connect IBM TechXchange | © 2026 IBM Corporation 9
10. Your data lives…well...everywhere… Reuse integrations Set data in motion Maintain loose coupling IBM TechXchange | © 2026 IBM Corporation 10
11. Connect makes Kafka your enterprise data backbone IBM TechXchange | © 2026 IBM Corporation 11
12. Process IBM TechXchange | © 2026 IBM Corporation 12
13. Raw events aren’t very useful Turn raw events into useful streams Add context, state, and time Focus on logic, not runtime operations Filter, join, enrich, aggregate, and transform data as it arrives. Real applications don’t process every event in isolation Managed Flink service allows your team to focus on the processing logic IBM TechXchange | © 2026 IBM Corporation 13
14. Ⓡ Apache Flink - the processing layer Compute pool IBM TechXchange | © 2026 IBM Corporation Identity and access Output data product Workload boundaries 14
15. Govern IBM TechXchange | © 2026 IBM Corporation 15
16. Scale data sharing without losing trust or control Trust the data Safely evolve schemas and data contracts Know, discover, and control data streams Define structure, compatibility, metadata, and quality expectations Allow schemas to change while protecting downstream applications Insight into data origins, transformations, and destinations IBM TechXchange | © 2026 IBM Corporation 16
17. Governance guardrails around the DSP Stream quality IBM TechXchange | © 2026 IBM Corporation Stream catalog Stream lineage 17
18. Let’s build something IBM TechXchange | © 2026 IBM Corporation 18
19. Personas of the DSP Citizen developer Data engineer Data scientist Integration specialist Operator Data teams IBM TechXchange | © 2026 IBM Corporation 19
20. Provision the data streaming platform Confluent Cloud Environment API Keys Service Accounts Role Bindings Flink Compute Pool(s) Kafka Cluster(s) Stream Governance IBM TechXchange | © 2026 IBM Corporation 20
21. Confluent Cloud Terraform Provider IBM TechXchange | © 2026 IBM Corporation 21
22. Q&A Sandon Jacobs IBM, Senior Developer Advocate https://linktr.ee/sandonjacobs IBM TechXchange | © 2026 IBM Corporation 22