How does the JDBC connection work? And what could go wrong with it? When it comes to streaming database events into Apache Kafka, the JDBC connector usually represents the first choice for its flexibility and the ability to support a wide variety of databases without requiring custom code. As an experienced data analyst, Francesco Tisiot (Senior Developer Advocate, Aiven) delves into his experience of streaming Kafka data pipeline with JDBC source connector and explains what could go wrong. He discusses alternative options available to avoid these problems, including the Debezium source connector for real-time change data capture.

Implementing an event-driven data pipeline can be challenging, but doing so within the context of a legacy architecture is even more complex. Having spent three years building a streaming data infrastructure and being on the first team at a financial organization to implement Apache Kafka event-driven data pipelines, Danica Fine (Senior Developer Advocate, Confluent) shares about the development process and how ksqlDB and Kafka Connect became instrumental to the implementation.

Getting data from a database management system (DBMS) into Apache Kafka in real time is a subject of ongoing innovation. John Neal (Principal Solution Architect, Qlik) and Adam Mayer (Senior Technical Producer Marketing Manager, Qlik) explain how leveraging change data capture (CDC) for data ingestion into Kafka enables real-time data-driven insights.

Processing data in real time is a process, as some might say. Angela Chu (Solution Architect, Databricks) and Caio Moreno (Senior Cloud Solution Architect, Microsoft) explain how to integrate Azure, Databricks, and Confluent to build real-time data pipelines that enable you to ingest data, perform analytics, and extract insights from data at hand.

What’s it like being a Microsoft Azure Cloud advocate working with Apache Kafka® and change data capture (CDC) solutions? Abhishek Gupta would know!