This article explores Apache Flink's Materialized Table, a unified stream-batch architecture introduced in Flink 2.
Discover how China's booming EV market leverages Alibaba Cloud's Flink & Apache Paimon for real-time data processing.
Discover how Lalamove developed a scalable, cost-effective cloud-native streaming platform, leveraging Apache Flink and Paimon to enhance data management.
Explore how Apache Paimon addresses big data system challenges by offering a unified data lake storage solution.
Discover how to harness streaming data for AI-driven applications using Apache Flink, based on insights from the Flink Forward Asia 2024 keynote by Ashish Sharma and Ganireddy Jyothi Swaroop.
This article introduces a data processing workflow that integrates Realtime Compute for Apache Flink, EMR Serverless Spark, and Apache Paimon to enable real-time data ingestion.
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.
The article introduces the development history, main scenarios, technical principles, performance tests, and future plans of the StarRocks + Apache Paimon lakehouse analysis.
Learn about Apache Flink, a distributed data processing engine for real-time analytics. Explore its features, use cases, and comparisons with other frameworks like Kafka and Spark.
