.png.6dd3056f38e93712a18d153891e8e0fc.png.1dbd1e5f05de09e66333e631e3342b83.png.933f4dc78ef5a5d2971934bd41ead8a1.png)
Search the Community
Showing results for tags 'flink'.
Found 5 results
-
Over the past few years, Apache Kafka has emerged as the leading standard for streaming data. Fast-forward to the present day: Kafka has achieved ubiquity, being adopted by at least 80% of the Fortune 100. This widespread adoption is attributed to Kafka's architecture, which goes far beyond basic messaging. Kafka's architecture versatility makes it exceptionally suitable for streaming data at a vast "internet" scale, ensuring fault tolerance and data consistency crucial for supporting mission-critical applications. Flink is a high-throughput, unified batch and stream processing engine, renowned for its capability to handle continuous data streams at scale. It seamlessly integrates with Kafka and offers robust support for exactly-once semantics, ensuring each event is processed precisely once, even amidst system failures. Flink emerges as a natural choice as a stream processor for Kafka. While Apache Flink enjoys significant success and popularity as a tool for real-time data processing, accessing sufficient resources and current examples for learning Flink can be challenging. View the full article -
The Flink Operator is a control plane that deploys and manages the entire lifecycle of Apache Flink applications. The goal of the Flink Operator is to manage applications as a human operator would. It handles cluster startup, deploys jobs, updates apps, and resolves prevalent problems. It can automate operational tasks and comprehensively manage Apache Flink applications. View the full article
-
Apache Flink Kinesis Consumer now supports Enhanced Fan Out (EFO) and the HTTP/2 data retrieval API for Amazon Kinesis Data Streams. EFO allows Amazon Kinesis Data Streams consumers to scale by offering each consumer a dedicated read throughput up to 2MB/second. The HTTP/2 data retrieval API reduces latency of data delivery from producers to consumers to 70 milliseconds or better. In combination, these two features allow you to build low latency Apache Flink applications that utilize dedicated throughput from Amazon Kinesis Data Streams. View the full article -
Amazon Kinesis Data Analytics for Apache Flink now provides access to the Apache Flink Dashboard, giving you greater visibility into your applications and advanced monitoring capabilities. You can now view your Apache Flink application’s environment variables, over 120 metrics, logs, and the directed acyclic graph (DAG) of the Apache Flink application in a simple, contextualized user interface. View the full article
-
You can now build and run streaming applications using Apache Flink version 1.11 in Amazon Kinesis Data Analytics for Apache Flink. Apache Flink v1.11 provides improvements to the Table and SQL API, which is a unified, relational API for stream and batch processing and acts as a superset of the SQL language specially designed for working with Apache Flink. Apache Flink v1.11 capabilities also include an improved memory model and RocksDB optimizations for increased application stability, and support for task manager stack traces in the Apache Flink Dashboard. View the full article
-
Forum Statistics
63.6k
Total Topics61.7k
Total Posts