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We measure the performance of Flink for various types of streaming applications and put it into perspective by running the same series of experiments on Apache Storm, a widely used low-latency stream processor. In this article, we dig in deeper into how Flink's novel checkpointing mechanism works, and how it supersedes older architectures for streaming fault tolerance and recovery.
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In recent articles, we introduced Apache Flink™ as a scalable stream processing engine that provides exactly this combination of properties. Infrastructures based on streaming data not only enable new types of latency-critical applications and give more actual operational insights through more up-to-date views of the processes they also have the potential to make classical data warehousing setups radically more simple and flexible the same time.Ī crucial piece of a streaming infrastructure is a stream processor that can deliver high throughput across a wide spectrum of latencies and strong consistency guarantees even in the presence of stateful computations. Several companies are transitioning parts of their data infrastructure to a streaming paradigm as a solution to increasing demands for real-time access to information. The popularity of stream data platforms is skyrocketing. Join different Meetup groups focusing on the latest news and updates around Flink Join the biggest Apache Flink community event! Take a sneak peek at Flink events happening around the globeĮxplore upcoming Ververica Webinars focusing on different aspects of stream processing with Apache Flink Learn from the original creators of Apache Flink with on-demand, public and bespoke courses
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Technical articles about how to use and set up Ververica PlatformĬhoose the right Ververica Platform Edition for your needsĪn introductory write-up about Stream Processing with Apache FlinkĮxplore Apache Flink's extensive documentation User Guides & Release Notes for Ververica Platform Apache Flink-powered stream processing platform