This article is compiled from a presentation by Yunfeng Zhou, a Senior Development Engineer at Alibaba Cloud and an Apache Flink Contributor, during the Apache Asia CommunityOverCode 2024 event.
This article focuses on the advanced capabilities of PolarDB-X CDC.
This article introduces the architecture and release notes of PolarDB-X V2.4 and the new clustered columnar index (CCI) feature in PolarDB-X v2.4.
This article delves into PolarDB-X 2.0's Global Binlog feature and its backup and restoration functionalities, highlighting their role in preventing data silos and safeguarding database information.
This article provides a detailed guide on implementing Change Data Capture (CDC) using Debezium and ApsaraMQ for Apache Kafka
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.
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.
Learn about stream processing, its applications, challenges, and Alibaba Cloud's Realtime Compute for Apache Flink solution for real-time data analysis.
This article introduces how to use EventBridge to build CDC applications from the aspects of CDC, CDC's application on EventBridge, and several best practice scenarios.
This article reviews the history of MongoDB and explains the new features of MongoDB 6.0.
This article discusses real-time data warehouse construction and offers examples of using Flink CDC and StarRocks for real-time links and data updates.
Part 3 of this 10-part series introduces the code engineering structure of GalaxyCDC and shows the construction process of the local development and debugging environment.
This article introduces OceanBase and explains the application scenarios of Flink CDC and OceanBase.
This article focuses on the processing logic of Flink CDC.
Part 5 of this 5-part series explains how to use Flink CDC and Doris Flink Connector to monitor data from MySQL databases and store data in the tables in real-time.
Part 4 of this 5-part series shares the details of the Flink CDC version 2.1 trial process, including troubleshooting experiences and internal execution principles.
Part 3 of this 5-part series shows how to use Flink CDC to build a real-time database and handle database and table shard merge synchronization.
Part 2 of this 5-part series explains how to realize Flink MongoDB CDC Connector through MongoDB Change Streams features based on Flink CDC.
Part 1 of this 5-part series explains how to use Flink CDC to simplify the entry of real-time data into the database.
