by Danielle Bingham | April 22, 2024

What is Change Data Capture, How Does it Work, & What Are its Benefits?

Organizations are under pressure to produce immediate insights from their data to stay competitive. Traditional ETL (extract, transform, load) pipelines, while powerful, often struggle to process data fast enough to generate up-to-the-minute analysis—there is just too much data.

This is where change data capture (CDC) comes in. Change data capture is a technique that automatically identifies changes made to data at the source, captures the changes, and records them for later storage or analysis. By focusing only on the data that’s recently changed, CDC significantly reduces the resource burden and processing time compared to replicating all the data in the source. This enables businesses to significantly minimize data latency and streamline their data processing workflows.

In this article, we will explain CDC, how it works, and its benefits. We’ll also go over some typical use cases so you can speed up your data to provide fast, actionable insights.

What is change data capture?

CDC is a sophisticated data integration technique designed to identify and capture only the changes made to a data source since the last capture point. Unlike traditional data synchronization methods that often require batch processing of large datasets, ETL processes that implement CDC focus on incremental changes, making them more efficient and less resource intensive.

CDC operates by continuously monitoring changes in data sources, such as inserts, updates, and deletions. When a change is detected, CDC captures the change and immediately provides this data for processing. This mechanism ensures that data in downstream systems is consistently up-to-date, reflecting the latest state of the source system without the need to load the entire dataset.

Traditional ETL methods without CDC capabilities involve extracting entire datasets at scheduled intervals, transforming the data for use, and then loading it into a data warehouse or similar system. This process can be slow and cumbersome, especially with large volumes of data. In contrast, CDC only deals with the data that has changed, significantly reducing the data volume that needs processing and enhancing the speed of data updates.

CDC allows organizations to gain insights faster, maximizing their data’s value without increasing the task load on their teams or infrastructure. Processes become leaner, and teams can put their energies into more strategic initiatives.

The different change data capture methods

Several CDC methods exist, each offering advantages and trade-offs. The choice depends on many factors, including the data architecture, the volume of data, and the amount of latency the organization can tolerate to get the most value with the least resources. Just a few are listed below:

• Trigger-based CDC captures changes when an ‘event’ is triggered. Stored procedures (triggers) defined in the CDC process automatically execute when an insert, update, or delete event is detected. These changes are then stored in a separate change table in the same data source. This is useful for tracking specific changes, but it increases processing load, which can impact performance.
• Table deltas involve comparing snapshots of tables over time to detect changes. This method can be simpler to implement but might require more processing power and storage since it involves creating and comparing full table copies at different times.
• Log-based CDC captures changes as they are written to the transaction logs without adding to the processing load. This non-intrusive method records real-time changes on the same log, which improves speed and accuracy, making it one of the most reliable CDC types.
• Audit columns are used to track basic information, such as timestamps, versions, or user IDs, in a table. Not all databases support audit columns, but this method is simple and straightforward. They can be built with the application’s native logic, and don’t require additional tooling.
• Query-based CDC involves periodically querying the database for changes, often using timestamp columns or incremental keys. While this method is relatively easy to implement, it can be less efficient and more intrusive than log-based methods because it requires active querying of the database.

What’s the difference between change tracking and change data capture?

Change tracking and CDC are both used to monitor and record changes within a data source, but they serve different informational needs. The primary difference lies in the level of detail each method provides about the changes.

Change tracking flags the occurrence of changes, but it does not capture the data that was changed. Instead, it provides just enough information for an application or service to know that it needs to update its data. This approach is typically used when simply knowing that a change has happened is sufficient, such as synchronizing incremental updates between databases.

CDC, on the other hand, records the specific changes, capturing exact details like what data was inserted, updated, or deleted. It’s indispensable for obtaining a complete historical record for analysis, reporting, or replication. It’s ideal for more complex data management tasks where understanding the specifics of each data modification can drive significant business insights or operational efficiency.

How does change data capture work?

The process is pretty simple: CDC continuously monitors a data source for changes in the source system and replicates those changes to the target system in real time. This keeps the source and target systems synchronized, ensuring that the most recent data is available for analysis, reporting, and accurate data backups.

The general workflow looks something like this:

1. Monitor: CDC systems continuously scan the source database's log files or use database triggers to detect changes.
2. Capture: Once changes are detected, CDC software captures the complete details of these changes, including the before and after values of the data.
3. Replicate: The captured changes are then replicated to the target system. This replication can occur almost instantly or at defined intervals, depending on the setup.

There are two different ‘flavors’ of CDC system operation:

Push-based CDC

In push-based CDC, the source system is set up to actively monitor for changes. When a change is detected, it is immediately pushed to the target system as it happens. This is usually done in real-time event streaming or by setting up triggers. In this scenario, the target system is passive—it just waits for the changes to be served by the source system. Only then does the target receive and record the change. This method drastically minimizes latency with near-instant synchronization. Push-based CDC is a crucial method for gaining real-time updates for quick analysis.

The downside, however, is that push-based CDC relies on the target system's readiness to accept changes. If the target system is unavailable for any reason, it can’t receive the changes, so the data is lost. This can be mitigated with intermediate queue-based systems that put the changes into a queue, saving them for when the target is available. The target system can then process the updates from the point of disruption and stay on track with the source system.

Pull-based CDC

Pull-based CDC turns the tables, requiring the target system to periodically query the source system to pull the updates at pre-determined intervals. This reduces the workload on the source system and can be set up to minimize load during peak hours or to accommodate systems where real-time synchronization is not as critical.

The primary benefit of pull-based CDC is its capability in error scenarios. Similar to the queue-based system in the push mode, the target can track what has already been processed and easily resume operations after disruptions. But it comes at the price of increased latency since the data is fetched at intervals rather than being pushed in real time.

5 key benefits of change data capture

CDC adds several advantages to traditional ETL processes, which can make a difference when data needs to be moved quickly. Here are some key benefits:

1. Minimizes discrepancies and ensures data integrity: CDC ensures that only the most recent changes are captured and transmitted, reducing the risk of data discrepancies between the source and target systems. This continuous synchronization helps maintain data integrity across different storage environments, making sure that all systems reflect the most current data state.
2. Provides near real-time data access: By capturing and replicating changes as they happen, CDC allows businesses to access and analyze data in near real-time. This is critical for decision-making processes that rely on the latest information, such as dynamic pricing, inventory management, and customer relationship management (CRM).
3. Reduces data processing time: CDC eliminates the need to perform resource-intensive and time-consuming bulk data loads. This accelerates data processing and reduces the load on network and database resources, reducing costs and improving system performance.
4. Simplifies data movement: With CDC, moving data is significantly less complex. Organizations can easily transfer changes across different platforms and environments, eliminating the overhead of managing complete dataset transfers and reducing potential points of failure.
5. Adapts to changing data volumes and business needs: CDC is highly scalable and can adjust to increasing volumes of data without major reconfigurations. This adaptability is great for businesses experiencing growth or those with fluctuating data usage patterns.

Typical use cases of change data capture

CDC is extremely versatile, with applications spanning across industries. From continuous replication and monitoring manufacturing processes, to tracking customer interactions, CDC is a straightforward solution to a number of data management functions. Here are a few use cases:

• Continuous data replication: CDC is critical for continuous data replication between production databases and backup systems, or between operational databases and data warehouses. This ensures high availability and disaster recovery by keeping secondary systems up-to-date with the latest data changes from the primary systems.
• Inventory change tracking: The retail and manufacturing sectors utilize CDC to track inventory changes in real time, allowing organizations to maintain the right amount of inventory across multiple locations.
• Integration with microservices: In modern application architectures, CDC facilitates the integration of microservices by providing each service with access to relevant data changes. This decouples services and reduces dependencies, allowing for more scalable and resilient application ecosystems.
• Manufacturing process monitoring: CDC lets manufacturers monitor production processes as they happen by capturing changes in operational data. This real-time data flow helps optimize production schedules, maintenance, and quality control, reducing downtime and making processes more efficient.
• Cloud adoption: More organizations than ever are migrating to cloud-based platforms, and CDC helps support those efforts, providing seamless data transfer to the cloud. It ensures that data migrated to cloud environments is consistent and synchronized with on-premises systems, enabling a smooth transition and continuous data integrity.
• Customer interaction tracking: CDC helps businesses track customer interactions across various channels in real time, providing valuable insights into customer behavior and preferences.

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