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Community Blog What is Change Data Capture (CDC)?

What is Change Data Capture (CDC)?

Change Data Capture (CDC) detects and captures data changes as they occur in source systems, such as databases or applications.

Introduction to Change Data Capture (CDC)

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 Methods

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.

Log-based Change Data Capture

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.

Trigger-based Change Data Capture

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.

Timestamp-based Change Data Capture

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.

Use Cases of Change Data Capture

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.

Evolution of CDC: Introduction to Flink CDC

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.

Key Features and Advantages of Flink CDC

  • Change Data Capture: Flink CDC excels in distributed scanning of historical data and seamlessly transitions to change data capturing. It employs an innovative incremental snapshot algorithm, ensuring that the switch to CDC does not lock the database, thus maintaining the system's availability and performance.
  • Schema Evolution One of the standout features of Flink CDC is its capability to automatically create downstream tables based on inferred table structures from upstream tables. It also applies upstream DDL (Data Definition Language) to downstream systems during the change data capturing process, enabling smooth and automatic adaptation to schema changes.
  • Streaming Pipeline: Flink CDC operates in streaming mode by default, providing industry-leading sub-second end-to-end latency in real-time binlog synchronization scenarios. This feature ensures that downstream businesses have access to the freshest data possible, enabling them to make time-sensitive decisions with confidence.
  • Data Transformation: Flink CDC is set to introduce support for a range of ETL (Extract, Transform, Load) operations, including column projection, computed column, filter expression, and classical scalar functions. This will empower users to perform complex data manipulations directly within the CDC process, enhancing the tool's versatility and utility.
  • Full Database Sync: The tool offers the capability to synchronize all tables of a source database instance to downstream systems in a single job. This is achieved by configuring the captured database list and table list, streamlining the process of data synchronization and reducing the complexity of managing multiple jobs.
  • Exactly-Once Semantics: Flink CDC ensures exactly-once processing of CDC events, even in the event of job failures. This feature guarantees data integrity and consistency, providing peace of mind to users that their data is accurate and reliable.

Why Change Data Capture with Alibaba Cloud

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.

PostgreSQL MongoDB MySQL data consistency Data Synchronization Distributed Computing Alibaba Cloud Data Processing Apache flink Data Warehousing Realtime Compute Data Management ETL Data Lakes Data Transformation Change Data Capture CDC Flink CDC Real-Time Analytics Real-time Data Integration Infrastructure Management HR Processes Schema Evolution Streaming Pipeline Exactly-Once Semantics Open-Source Connectors Data Infrastructure Real-Time Data Integration.
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