Cloud Computing, Data Analytics

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The Impact of CDC on Production Databases and Real-Time Systems

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CDC stands for Change Data Capture, and many organizations across various sectors manage production databases, wherein most data remain relatively static over time. Daily alterations and updates represent only a small fraction of stored data. Within these organizations, the implementation of change data capture (CDC) data replication holds the greatest potential for benefit.

Businesses heavily depend on immense and ever-evolving datasets to make crucial decisions. In certain systems, such as autonomous vehicles, instantaneous feedback derived from data is pivotal to their operation. Ensuring seamless efficiency across every aspect of the data pipeline is imperative for these businesses and systems to operate optimally.

Understanding CDC

Change Data Capture (CDC) data replication involves the real-time or near real-time data transfer between two databases, copying only the newly added or modified data. This method contrasts with snapshot replication, where complete snapshots of one database are repeatedly transferred to another. While snapshot replication is appropriate for organizations requiring the preservation of specific data snapshots over time, it is computationally intensive and costly. CDC offers significant savings in processing resources for organizations that do not need this preservation.

CDC is particularly useful in scenarios where data needs to be replicated across different databases or systems while minimizing the amount of data transferred. By capturing only the changes that occur, CDC reduces the network bandwidth and processing power required for data replication compared to traditional methods such as full data dumps or snapshot replication.

Now, we will move further and understand the use cases for Change Data Capture:

  1. Data Warehousing: CDC is extensively used in data warehousing environments, and it helps to keep the data warehouse updated with the latest changes from operational databases. This ensures that analytics and reporting on the data warehouse reflect the most recent data, enabling timely and accurate decision-making.
  2. Real-time Analytics: Organizations often require real-time insights into their operational data for monitoring, trend analysis, and decision-making. CDC facilitates the continuous capture and delivery of data changes to analytical systems, allowing businesses to derive actionable insights without delay.
  3. Data Integration: CDC is instrumental in integrating data from disparate sources and systems. By capturing and replicating changes from source databases to target systems in real-time, CDC ensures that all data remains synchronized across the organization, enabling a unified view of business operations.
  4. Replication for High Availability: CDC replicates data across multiple databases or servers for high availability and disaster recovery. Organizations can minimize downtime and ensure business continuity during system failures or disasters by replicating changes to standby databases or servers.
  5. Data Migration and ETL: CDC is used in data migration and Extract, Transform, and Load (ETL) processes to transfer data between different systems or storage platforms efficiently. By capturing only the changes made to the source data, CDC minimizes the time and resources required for data migration and ETL, enabling faster and more efficient data transfers.

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Common CDC Techniques

  1. Log-Based CDC technique: It captures and replicates data changes by monitoring the transaction log (also known as the redo log or write-ahead log) of a database management system (DBMS). Instead of directly inspecting the tables for changes, log-based CDC extracts changes from the transaction log, which records all modifications made to the database.


  • Low Overhead: Log-based CDC typically imposes minimal overhead on the source system because it reads the transaction log directly rather than querying tables. This ensures that capturing changes does not impact the performance of the source database.
  • Real-time or Near Real-time Replication: Log-based CDC enables real-time or near real-time replication of data changes. Since the transaction log is written as transactions occur, changes can be captured and replicated almost instantly, ensuring that target systems remain synchronized with the source.
  • Support for Transactional Integrity: Log-based CDC maintains transactional integrity during data replication by capturing changes as atomic transactions from the transaction log. This ensures that changes are applied in the same order and consistently as they occurred in the source database.
  1. Trigger-Based CDC technique:

Trigger-based CDC represents a hybrid approach, combining elements of the preceding techniques. It entails setting up triggers to detect specific changes within a table, subsequently recording them into a distinct table for tracking. It is this intermediary table from which the modifications are propagated to the target system.


  • Flexibility: Enables customization of the types of changes to capture and the methods of capture, akin to query-based CDC, encompassing the deletion of rows as seen in log-based CDC.
  • Low Latency: Every trigger activation constitutes an event, which can be promptly processed, facilitating real-time or near-real-time processing.


Change Data Capture (CDC) is a pivotal technology in data management, allowing organizations to capture and replicate data changes in real-time or near real-time. Throughout this exploration, we’ve delved into the three primary techniques of the CDC: log-based and trigger-based.

Drop a query if you have any questions regarding CDC and we will get back to you quickly.

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1. What is Change Data Capture (CDC), and why is it important?

ANS: – CDC is a technology used to detect and capture changes made to data in databases. It’s crucial because it allows organizations to replicate these changes in real-time or near real-time, ensuring that data across systems remains synchronized and up to date.

2. What are some common use cases for the CDC?

ANS: – Common use cases for CDC include data warehousing, real-time analytics, data integration, replication for high availability, microservices architecture, and data migration/ETL processes.

3. What are the best practices for implementing CDC?

ANS: – Best practices for implementing CDC include ensuring data consistency and integrity, optimizing performance, monitoring and troubleshooting replication processes, and staying updated on CDC technologies and methodologies advancements. Additionally, organizations should adhere to security and compliance standards to protect sensitive data during replication.

WRITTEN BY Parth Sharma



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