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.
By moving away from batch processing to streaming data pipelines with Kafka, data can be distributed with increased data scalability and resiliency. Kafka decouples the source from the target systems, so you can react to data as it changes while ensuring accurate data in the target system.
In order to transition from monolithic micro-batching applications to real-time microservices that can integrate with a legacy system that has been around for decades, Danica and her team started developing Kafka connectors to connect to various sources and target systems.
As a final tip, Danica suggests breaking algorithms into process steps. She also describes how her experience relates to the data pipelines course on Confluent Developer and encourages anyone who is interested in learning more to check it out.
If there's something you want to know about Apache Kafka, Confluent or event streaming, please send us an email with your question and we'll hope to answer it on the next episode of Ask Confluent.Email Us