Join Dan Vega and DaShaun Carter for the latest updates from the Spring Ecosystem. In this episode, Dan and DaShaun are joined by Spring Cloud engineer and Spring Cloud Data Flow expert, Corneil du Plessis. Spring Cloud Data Flow is a powerful platform for data workflows. You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform.

Show Notes

• Spring Cloud Data Flow

• Corneil du Plessis

Join Dan Vega and DaShaun Carter for the latest updates from the Spring Ecosystem. In this episode, Dan and DaShaun are joined by Mark Nelson and Anders Swanson from Oracle to discuss Spring Cloud Oracle. At the end of this episode, you will understand how Spring Cloud Oracle simplifies integration with Oracle Database and OCI services, including UCP, vector databases, and more. You can participate in our live stream to ask questions or catch the replay on your preferred podcast platform.

Show Notes:

1. Introduction to Spring Cloud Oracle and its purpose
2. Overview of Oracle Cloud Infrastructure (OCI) free tier
3. Discussion on Universal Connection Pool (UCP) vs HikariCP
4. Oracle's vector database support and Spring AI integration
5. Oracle's in-database message broker and Spring Cloud Stream binder
6. Virtual threads support in Java 21
7. Server-side connection pooling for microservices
8. JSON collections and relational duality views in Oracle Database
9. Observability features in OCI
10. Upcoming OpenTelemetry support for distributed tracing
11. Spring CLI and project templates for Oracle Cloud
12. Test container support for Oracle Database
13. Upcoming open-rewrite recipes for HikariCP to UCP migration

Resources:

• Oracle Cloud free tier: https://www.oracle.com/cloud/free/
• Spring Cloud Oracle GitHub: https://github.com/oracle/spring-cloud-oracle

Key Takeaways:

• Spring Cloud Oracle simplifies integration between Spring applications and Oracle Database/OCI services
• Oracle offers a generous free tier for developers to explore and test OCI services
• Upcoming features include improved observability, distributed tracing, and migration tools

In this episode of Spring Office Hours, hosts Dan Vega and DeShaun Carter interview Chris Bono, a Spring team member who works on Spring Cloud Dataflow and Spring Pulsar. They discuss streaming data, comparing Apache Kafka and Apache Pulsar, and explore the features and use cases of Spring Cloud Stream applications. Chris provides insights into the architecture of streaming applications, explains key concepts, and highlights the benefits of using Spring's abstraction layers for working with messaging systems.

Show Notes:

1. Introduction to Chris Bono and his work on Spring Cloud Dataflow and Spring Pulsar
2. Comparison between Apache Kafka and Apache Pulsar
3. Overview of Spring Cloud Stream and its binders
4. Explanation of source, processor, and sink concepts in streaming applications
5. Introduction to Spring Cloud Stream Applications project
6. Discussion on Change Data Capture (CDC) and its importance in streaming
7. Exploration of various sources, processors, and sinks available in Spring Cloud Stream Applications
8. Mention of KEDA (Kubernetes Event-driven Autoscaling) and its potential use with Spring Cloud applications
9. Upcoming features in Spring Pulsar 1.2 release
10. Importance of community feedback and using GitHub discussions for feature requests and issue reporting

The podcast provides a comprehensive overview of streaming data concepts and how Spring projects can be used to build efficient streaming applications.