Change Data Capture

Definition of Change Data Capture

Change Data Capture (CDC) is a technology used to monitor and track changes made to a database, capturing incremental modifications in real-time or near-real-time. By identifying and processing only the altered data, CDC allows for efficient data integration, simplifying the ETL (Extract, Transform, Load) process. This technology enables efficient data synchronization, replication, and warehousing, reducing latency and resource consumption.


The phonetic pronunciation of “Change Data Capture” is: – Change: /ʧeɪndʒ/- Data: /ˈdeɪtə/ or /ˈdætə/ – Capture: /ˈkæptʃər/

Key Takeaways

  1. Change Data Capture (CDC) enables efficient data synchronization and integration by tracking and capturing changes in source databases.
  2. CDC reduces the resources required for ETL processes, improves data accuracy, and provides near real-time data availability for analysis or replication.
  3. Popular database systems like SQL Server, Oracle, and PostgreSQL have built-in support for Change Data Capture, making it an essential tool for maintaining data consistency across applications and systems.

Importance of Change Data Capture

Change Data Capture (CDC) is an important technology term and a crucial process in data integration, as it allows for the efficient tracking, identification, and capturing of changes made to data sources, such as databases or data warehouses.

By continuous monitoring of these changes, CDC ensures the accuracy, consistency, and real-time synchronization of data across multiple systems without compromising on performance.

This leads to better decision-making capabilities, analysis, and reporting by providing organizations with the most up-to-date information.

Moreover, CDC contributes to minimizing data conflicts, reducing latency and network load, and promoting seamless integration among diverse applications, ultimately bolstering data management and the overall success of an organization’s data-driven strategies.


Change Data Capture (CDC) serves as a critical component for organizations that aim to maintain high-quality data consistency and integrity, particularly when handling vast amounts of information. As the crux of modern data-driven practices, CDC’s primary purpose is to monitor and record all alterations made to a source database in real-time, such as additions, updates, and deletions. By capturing these modifications, CDC facilitates efficient and seamless data synchronization across multiple systems and applications, minimizing the likelihood of data discrepancies amidst ongoing transactions.

Consequently, organizations can maintain up-to-date insights into their operations and make well-informed decisions, while simultaneously reducing the risks associated with data loss or inaccuracies. In addition to the advantages that CDC offers in data synchronization, it also aids organizations in streamlining their extract, transform, and load (ETL) processes – the foundation of data warehousing and analytics workflows. By identifying granular changes in a given dataset, CDC eliminates the need for full-scale data extraction, thereby considerably reducing the resource-intensive workload and time involved in traditional ETL processes.

This fine-tuning capability for data integration consolidates a compact yet accurate snapshot of the evolving data landscape. This in turn enables businesses to achieve swift and precise analysis of their operational performance, supporting organizational agility and responsiveness. As a result, the implementation and leveraging of Change Data Capture techniques empower organizations to refine data management and drive transformative growth in an increasingly data-driven world.

Examples of Change Data Capture

Change Data Capture (CDC) is a technology used to identify and capture changes made to a database and apply them to another data storage system. This allows tracking the changes without compromising data accuracy and consistency. Here are three real-world examples of companies or platforms using Change Data Capture:

Salesforce:Salesforce, a leading customer relationship management (CRM) platform, employs Change Data Capture technology to track and replicate changes in real-time. As organizations continually update and modify their customer data, CDC enables Salesforce to capture these changes and update them across various systems and data warehouses. This ensures that customer information is always accurate and up-to-date within the platform.

Wells Fargo:Wells Fargo is a multinational financial services company that relies on Change Data Capture to ensure data consistency in its banking systems. Whenever a user conducts a transaction, such as transferring money, a change is logged into the transactional databases. CDC technology detects these changes and updates them across Wells Fargo’s data storage system, ensuring any business or customer-facing applications reflect the latest information.

Google Cloud Platform’s Datastream:Google Cloud Platform offers a fully managed, serverless Change Data Capture service called Datastream. This service allows businesses to synchronize data across various databases and storage systems, such as Cloud Spanner and BigQuery. By employing CDC technology, Datastream tracks the changes in real-time, enabling data analytics, backup, and other applications to work with the most recent and accurate data.These examples demonstrate the importance of Change Data Capture in maintaining data accuracy and workforce efficiency across a wide range of industries and technologies.

Change Data Capture FAQ

What is Change Data Capture?

Change Data Capture (CDC) is a technology used to track and capture changes in data as they occur over time. It identifies modifications made to data in source systems and reflects the changes to the target data systems, ensuring data consistency across databases.

Why is Change Data Capture important?

Change Data Capture is crucial for several reasons, particularly in streamlining and automating data integration processes, reducing system impact during data synchronization or data warehousing, improving data accuracy, and ensuring near-real-time data availability for multiple applications.

How does Change Data Capture work?

Change Data Capture operates by constantly monitoring source systems for data changes, either via database logs or triggers. Upon detecting modifications, it captures the change information, processes it, and then updates the target systems accordingly. This ensures the continuous and automatic flow of the latest data between source and target systems.

What are some common use cases for Change Data Capture?

Common use cases for Change Data Capture include real-time data synchronization, data warehousing, data integration and analytics, event-driven architecture, and auditing and compliance.

What are the different Change Data Capture methods?

There are various methods for implementing Change Data Capture, such as log-based, trigger-based, and polling-based. Log-based CDC involves monitoring database transaction logs, trigger-based CDC uses triggers to capture changes, and polling-based CDC periodically checks for data modifications.

Related Technology Terms

  • Real-time Data Integration
  • Data Replication
  • Data Warehousing
  • Event-driven Architecture
  • Database Triggers

Sources for More Information


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