Databases

Change Data Capture

A man in a server room using a tablet to monitor change data capture

Change Data Capture (CDC) is a process that keeps track of any modifications made to data in a database. It essentially captures the “deltas,” like insertions, updates, and deletions, so you can take action based on those specific changes.

Why is Change Data Capture Important?

Change data capture (CDC) plays a crucial role in disaster recovery (DR) by enabling organizations to efficiently recover from data loss or system outages on the primary system. Here’s how:

Reduced Recovery Time

CDC captures only the changes made to data after an initial full sync, significantly reducing the amount of data that needs to be restored in the event of a disaster. This translates to much faster recovery times compared to traditional full backups.

Continuous Synchronization

CDC continuously tracks and replicates changes from the primary system to a secondary system. This ensures that the secondary system always has the latest data, minimizing data loss in case of an outage.

Scalability and Efficiency

CDC reduces the workload on the primary system by focusing only on changes, instead of transferring large amounts of data during backups. This makes it more scalable and efficient, especially for large datasets.

Change Data Capture Architecture

There are several different tpes of CDC architecture. The need for CDC arose from the demand for near real-time data feeds from operational transaction databases. These databases are designed to maximize transaction throughput, so a non-invasive approach is needed to get data changes to a reporting or analytical database with minimal performance degradation.

Log-Based CDC

Captures changes from the transaction logs of the source database. This is a very efficient way to capture changes, but it can be complex to implement and maintain.

Trigger-Based CDC

Implements triggers on the source database to capture changes. This is a simpler way to implement CDC than log-based CDC, but it can be less efficient and can have a performance impact on the source database..

Timestamp-Based CDC

Utilizes timestamps on the source data to track changes. This is a simple and efficient way to capture changes, but it can be less reliable than other methods, as it is possible for timestamps to be out of sync.

Query-Based CDC

Uses queries to the source database to capture changes. This is a flexible way to capture changes, but it can be less efficient than other methods.

Change Data Capture Use Cases

Change Data Capture (CDC) has a range of valuable use cases, each offering unique benefits for different data management scenarios. Here are some of the most common applications:

Data Replication and Synchronization

Data Warehouse Updates

Continuously send changes from your transactional databases to data warehouses for near real-time analysis. This reduces batch processing and improves data freshness for analytics and reporting.

Database Migration

Migrate data between databases efficiently by capturing changes in the source and replicating them to the target.

Cloud Migration

Move on-premises data to the cloud seamlessly by capturing changes and replicating them to cloud databases or data lakes.

Real-Time Applications and Analytics:

Event-Driven Architectures

Power event-driven architectures by capturing data changes and triggering downstream actions in real-time. This enables applications to react instantly to events and updates.

Streaming Analytics

Continuously feed data changes into streaming analytics platforms for real-time insights and anomaly detection.

Live Dashboards and Reporting

Update dashboards and reports with the latest data automatically as changes occur, offering near real-time insights.

Benefits of Change Data Capture

Change Data Capture (CDC) offers a wide range of benefits for businesses looking to improve data management and analysis. Here are some key advantages:

  • No more batch windows: CDC captures changes as they happen, eliminating the need for large, time-consuming batch processing. This means faster data updates and near real-time insights.
  • Improved data freshness: With data continuously flowing, you can access and analyze the latest information without delays, leading to more accurate and timely decision-making.
  • Faster database migrations: CDC can facilitate near-zero downtime migrations, minimizing business disruption during transitions.

How to Pick a CDC Architecture

The best type of CDC architecture for you will depend on your specific needs and requirements. Here are some factors to consider when choosing a CDC architecture:

  • The volume of data changes: If you have a high volume of data changes, you will need a CDC architecture that can handle the load. Log-based CDC is typically the best choice for high-volume data changes.
  • The latency requirements: If you need to capture changes in near real-time, you will need a CDC architecture that has low latency. Trigger-based CDC is typically the best choice for low-latency requirements.
  • The complexity of the implementation: If you have limited resources, you may want to choose a simpler CDC architecture, such as timestamp-based CDC.
  • The cost: Some CDC architectures are more expensive to implement and maintain than others.

The Actian Data Platform

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