Change Data Capture (CDC) is a fundamental technology in today's data-driven landscape, enabling real-time data consistency and seamless integration across hybrid and multi-cloud environments. CDC detects and captures data changes as they occur in source systems, such as databases or applications. This approach ensures that businesses can stay up-to-date with the latest information and make timely decisions based on current data, a necessity that traditional batch processes cannot fulfill due to inherent delays.
Change Data Capture (CDC) can be implemented using various methods, each with its own approach to capturing data changes. The three primary methods are log-based CDC, trigger-based CDC, and timestamp-based CDC.
This method involves monitoring the transaction logs of the database for changes. When a change occurs, such as an insertion, update, or deletion, the log captures the change and records the before and after images of the data. The change is then propagated to the downstream systems for further processing. Log-based CDC is efficient as it only captures the data that has changed since the last replication, reducing the amount of data that needs to be processed.
In this method, database triggers are used to capture data changes. When a change occurs, such as an insertion, update, or deletion, the trigger captures the change and sends it to the downstream systems. Trigger-based CDC requires careful configuration and management of database triggers to ensure that all relevant changes are captured and propagated.
This method uses timestamps to track data changes. Each row in the database is assigned a timestamp when it is created or modified. When a change occurs, the timestamp is updated, and the change is propagated to the downstream systems. Timestamp-based CDC is less intrusive than log-based CDC and trigger-based CDC, as it does not require monitoring the transaction logs or configuring database triggers. However, it requires careful management of timestamps and may not be suitable for all types of data changes.
CDC finds extensive application in various domains. For instance, in HR processes, CDC captures employee data changes in real-time. This allows downstream systems like data warehouses, data lakes, and analytics platforms to consume and process the latest updates as they happen. By facilitating real-time data integration, synchronization, and analysis, CDC empowers organizations to optimize critical HR processes such as payroll, performance management, and workforce planning.
The evolution of CDC has led to the development of sophisticated tools like Flink CDC, which address the complexities of modern data processing. Flink CDC stands as a distributed data integration tool, crafted for both real-time and batch data processing. It introduces a novel approach to data integration through the use of YAML, which simplifies the description of data movement and transformation. This tool is designed to bring efficiency and ease to the complex realm of data integration, making it a preferred choice for professionals in the field.
Flink Change Data Capture (CDC) with Alibaba Cloud offers a suite of open-source connectors, compliant with the protocol of Apache Flink. These connectors are integrated into Alibaba Cloud's Realtime Compute for Apache Flink platform, providing organizations with the capability to capture data changes from various databases, including MySQL, PostgreSQL, MongoDB, and more. This ensures real-time data integration and synchronization across diverse data sources.
The key advantage of using CDC with Alibaba Cloud lies in its ability to support a wide range of databases and data types, enabling organizations to integrate and analyze diverse data sources seamlessly. Additionally, the open-source nature of these connectors means they can be customized and extended to meet specific business requirements. Furthermore, Alibaba Cloud's full-managed service ensures that organizations can focus on their core business while leaving the complexities of infrastructure management to Alibaba Cloud. This includes automatic scaling, continuous monitoring, and comprehensive security features, allowing businesses to leverage the power of real-time data integration without the overhead of maintaining their own data infrastructure.
Understanding Stream Processing: Real-Time Data Analysis and Use Cases
150 posts | 43 followers
FollowApache Flink Community - May 28, 2024
Apache Flink Community China - June 2, 2022
Apache Flink Community China - May 18, 2022
Alibaba Cloud Serverless - January 3, 2024
Alibaba Cloud Native Community - January 13, 2023
Alibaba Cloud Indonesia - March 23, 2023
150 posts | 43 followers
FollowRealtime Compute for Apache Flink offers a highly integrated platform for real-time data processing, which optimizes the computing of Apache Flink.
Learn MoreConduct large-scale data warehousing with MaxCompute
Learn MoreAlibaba Cloud provides big data consulting services to help enterprises leverage advanced data technology.
Learn MoreSecure and easy solutions for moving you workloads to the cloud
Learn MoreMore Posts by Apache Flink Community