Stream processing plays a critical role in the infrastructure stack for data streaming. Developers can use it to filter, join, aggregate, and transform their data streams on the fly to power real-time applications and streaming data pipelines. Among stream processing frameworks, Apache Flink has emerged as the de facto standard because of its performance and rich feature set. However, self-managing Flink (like self-managing other open source tools like Kafka) can be challenging due to its operational complexity, steep learning curve, and high costs for in-house support.

Join Test Case Scenario with Scott McAllister, Developer Advocate at ngrok. Explore ngrok's innovative approach to securing and exposing local services online, aiding businesses like restaurants with seamless order integrations. Learn how ngrok differs from traditional VPNs, saving developers time while ensuring security through OAuth integration.

Apache Flink® 1.19 is here! On behalf of the Flink community, David Anderson highlights key release updates with FLIPs for Legacy deprecations, Flink SQL, Observability, Flink Configuration, and Flink Connectors.

The Confluent Q1 ’24 Launch is packed with new features that enable customers to build, connect, and consume intelligent data pipelines seamlessly and securely Our quarterly launches provide a single resource to learn about the accelerating number of new features we’re bringing to Confluent Cloud, our cloud-native data streaming platform.