What Is Change Data Capture (CDC)? Definition, Benefits, and Use Cases

In today's fast-paced digital landscape, organizations rely heavily on data to make informed decisions, drive operational efficiency, and deliver exceptional customer experiences. However, ensuring access to up-to-date and accurate data can be challenging, especially when dealing with multiple systems and applications. This is where Change Data Capture (CDC) comes into play. It offers a powerful solution for seamlessly integrating and synchronizing data across different environments.

What is CDC?

Change Data Capture (CDC) is a technology that detects and captures data changes as they happen in the source systems, such as databases or applications. Unlike traditional batch processes that can introduce delays, CDC enables near real-time data integration and synchronization, allowing businesses to stay up-to-date with the latest information and make timely decisions based on current data.

To better understand CDC, let's look at an example. Imagine a table containing employee salary information at a company. When a new employee named "Alice Williams" joins the Finance department with a salary of $70,000, this change needs to be reflected in the table.

With CDC enabled, the system will automatically capture this new entry and generate a log or stream of the data modification. When this log or stream arrives at the target system (e.g., a data warehouse or analytics platform), it triggers an update to the target table, ensuring that both the source and target tables remain in sync and reflect the most up-to-date information.

The CDC log or stream captures the details of the "INSERT" operation, including the timestamp, the affected table name ("Employees"), and the actual data that was inserted. In this case, the log would contain the new employee's ID (1004), name, department, and salary information.

{
  "operation": "INSERT",
  "timestamp": "2023-06-01 09:15:22",
  "table": "Employees",
  "data": {
    "EmployeeID": 1004,
    "EmployeeName": "Alice Williams",
    "Department": "Finance",
    "Salary": 70000
  }
}

Additionally, if an existing employee receives a promotion or a salary increase, the CDC log might look like this:

{
  "operation": "UPDATE",
  "timestamp": "2023-06-15 11:30:47",
  "table": "Employees",
  "oldData": {
    "EmployeeID": 1002,
    "EmployeeName": "Jane Smith",
    "Department": "Marketing",
    "Salary": 75000
  },
  "newData": {
    "EmployeeID": 1002,
    "EmployeeName": "Jane Smith",
    "Department": "Marketing",
    "Salary": 80000
  }
}

In this case, the CDC log captures an "UPDATE" operation, showing both the old data (the employee details before the update) and the new data (the updated employee details, with the salary increased to $80,000).

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. It enables real-time data integration, synchronization, and analysis for HR processes such as payroll, performance management, and workforce planning.

Benefits of using CDC

So, what are the key benefits of implementing CDC in your data stack? Let's explore them:

Real-Time Integration: CDC captures and propagates data changes as they occur, ensuring downstream systems like data warehouses and lakes always have the latest, consistent data for real-time analytics, reporting, and decision-making.
Event-Driven Architectures: CDC treats data changes as events that can trigger downstream processes, workflows, or microservices. It publishes these events to queues or streams, enabling near real-time consumption and reaction.
Data Replication and Sync: For scenarios like high availability, disaster recovery, or performance optimization, CDC plays a crucial role in maintaining consistent data copies across distributed environments by capturing and applying changes.
Governance and Compliance: In regulated industries, CDC maintains audit trails, tracks data lineage, and documents changes to critical data sources for compliance purposes.
Hybrid and Multi-Cloud: As organizations adopt hybrid and multi-cloud strategies, CDC becomes essential for synchronizing data across different cloud environments, on-premises systems, and data stores, enabling seamless data sharing and integration.

Use cases of CDC

Let's now explore some real-world examples of how CDC can bring tangible benefits to your organization:

Financial Services: Data integrity is critical in finance. CDC synchronizes banking, trading, and accounting data in real-time for accurate financial decision-making.
Supply Chain and Logistics: These fast-paced industries require real-time visibility into inventory, shipments, and order fulfillment. CDC enables efficient data integration and synchronization, allowing you to track and respond to changes quickly, optimizing operations and customer satisfaction.
E-commerce and Retail: Exceptional customer experiences demand consistent, up-to-date product, pricing, and order data across all channels. CDC ensures seamless omnichannel experiences and informed decision-making.
Healthcare: Accurate, timely patient data is essential for quality care. CDC synchronizes electronic health record (EHR) systems in real-time, giving healthcare professionals the most current patient information.
Internet of Things (IoT): As IoT devices proliferate, ingesting and processing real-time data streams becomes crucial. CDC efficiently integrates IoT sensor and device data, enabling real-time monitoring, predictive maintenance, and data-driven decisions.

Popular CDC tools and technologies

Here are some popular Change Data Capture (CDC) tools widely used for real-time data integration and synchronization:

Debezium : An open-source CDC platform built on Apache Kafka. It captures and streams data changes from databases like MySQL, PostgreSQL, Oracle, SQL Server, and MongoDB, offering a scalable and reliable solution.
Maxwell CDC: A CDC tool that streams real-time changes from MySQL databases into platforms like Kafka, Kinesis, and others. It's known for its simplicity, ease of setup, lightweight architecture, and efficient streaming of row-level changes as JSON.
Canal: A CDC tool that streams real-time changes from MySQL databases to other systems, providing a unified changelog format schema and supporting JSON and protobuf message serialization.
Oracle GoldenGate: Oracle's CDC tool for real-time data replication and integration across Oracle databases and other databases like SQL Server, DB2, and Teradata, offering high-performance and low-impact data capture.
AWS Database Migration Service (DMS): A fully managed AWS service for database migration and replication, supporting CDC functionality for various databases, including Amazon RDS, Aurora, and on-premises databases.
Microsoft SQL Server Change Data Capture: A built-in CDC feature in SQL Server that allows capturing and tracking changes made to tables within SQL Server databases.

How RisingWave handles change data capture

RisingWave is a database purpose built for handling real-time data. One of its critical functionalities is the ingestion of real-time data, including change data capture (CDC) data.

RisingWave handles change data capture (CDC) by providing native CDC connectors for popular databases such as Postgres, MySQL, MongoDB, and Citus. These connectors simplify the technical stack and ensure consistency. Additionally, RisingWave can ingest CDC data delivered as streams in messaging system formats like Kafka, Pulsar, or Kinesis. However, for this, separate CDC tools are required to convert CDC data from databases into streams. For further information on how RisingWave ingests CDC data in these formats, please refer to the CDC via messaging systems documentation.