How can I do request-response communication with Apache Kafka? That’s one of the most common questions I get regularly. This blog post explores when (not) to use this message exchange pattern, the differences between synchronous and asynchronous communication, the pros and cons compared to CQRS and event sourcing, and how to implement request-response within the data streaming infrastructure.
Recognizing and handling errors is essential for any reliable data streaming pipeline. This blog post explores best practices for implementing error handling using a Dead Letter Queue in Apache Kafka infrastructure. The options include a custom implementation, Kafka Streams, Kafka Connect, the Spring framework, and the Parallel Consumer. Real-world case studies show how Uber, CrowdStrike, Santander Bank, and Robinhood build reliable real-time error handling at an extreme scale.
The rise of event streaming in financial services is growing like crazy. Continuous real-time data integration and processing are mandatory for many use cases. Apache Kafka is deployed across the financial services business departments for mission-critical transactional workloads and big data analytics. High scalability, high reliability, and an elastic open infrastructure are the key reasons for the success of Kafka. This blog post explores different use cases, architectures, and real-world examples in the FinServ sector.