Explore Apache Flink's Materialized Table for unified stream-batch ETL. Learn declarative data processing and overcome Lambda architecture challenges.
Build real-time MySQL-to-Kafka data pipelines using Flink CDC YAML without coding. Complete tutorial with whole database sync and schema changes.
Discover Apache Flink's evolution from real-time data processing to AI applications. Learn about Streaming Lakehouse, Apache Paimon, and Flash engine for next-gen AI.
Flink CDC enables real-time data integration with low latency, fault tolerance, and support for multiple databases, simplifying modern data workflows.
This article is based on the keynote speech delivered by Fajar Tontowi, Lead Data Engineer for Ingestion and Analytics at Mekari, at Flink Forward Asia in Jakarta 2024.
This article is based on the keynote speeches given by LI Jinsong, WU Xiangping, DI Xingxing, and WANG Yunpeng during Flink Forward Asia 2023.
This article describes an overview of the implementation principles and best practices of Hologres Binlog.
Change Data Capture (CDC) detects and captures data changes as they occur in source systems, such as databases or applications.
This article is based on a keynote speech given by Jark Wu, head of Flink SQL and Flink CDC at Alibaba Cloud, during Flink Forward Asia 2023.
Flink CDC 3.0 is a cutting-edge framework for real-time data integration, offering an efficient, scalable CDC solution with Apache Flink.
This article explores how AnalyticDB for MySQL uses Apache Hudi to ingest complete and incremental data from multiple CDC tables into data lakes.
This article explores the process of achieving one-click database synchronization from MongoDB to Paimon using Flink CDC.
This article discusses real-time data warehouse construction and offers examples of using Flink CDC and StarRocks for real-time links and data updates.
Mowen discusses the future of Apache Flink regarding its core capabilities of stream computing and improving the processing standards of the entire industry.
