Unified batch and stream processing of Flink is a well-established concept in the stream computing field.
This article is based on the keynote speeches given by LI Jinsong, WU Xiangping, DI Xingxing, and WANG Yunpeng during Flink Forward Asia 2023.
Uncover the advancements from Apache Hive to Hudi and Iceberg in stream computing, as our expert navigates the transformative landscape of real-time data lakes.
Discover Apache Paimon: the solution for real-time data processing, seamlessly integrating Flink & Spark for streaming & batch operations.
This article discusses the challenges and limitations of various solutions in CDC data analysis and describes how to use Flink and Iceberg to overcome them.
In this article, the author explains building a real-time data warehouse using Apache Flink and Apache Iceberg.
In this article, the author discusses how Apache Flink and Apache Iceberg have opened a new chapter in building a data lake architecture featuring stream-batch unification.
This article explains real-time data lakes based on Apache Flink and Apache Iceberg.
