Businesses live and breathe real-time data. But when your pipelines are built for periodic batch jobs, how do you get up-to-date information? Enter change data capture (CDC), which transfers small, real-time increments of content instead of bulk loads. By capturing only what’s changed since the last update, you can iterate without having to duplicate entire databases.

In this guide, we’ll show you why CDC is so effective at reducing resource drain, improving data transmission, and keeping mission-critical workloads online.

What’s change data capture (CDC)?

Change data capture is a technique that identifies changes in a database and records them to target systems. It’s a less resource-intensive method of real-time data integration, as you only need to process changes, rather than entire databases. 

Especially in machine learning or real-time analytics use cases, CDC-style data replication helps power insights without introducing unnecessary latency. For mission-critical applications that can’t accommodate the downtime periods required for batch processing, CDC is a reliable option. You can closely align target systems and source databases with near-real-time data availability, all without disrupting your environments.

How does change data capture work?

CDC works by detecting changes in a source system and then transmitting that information to your destinations. Although there are a few different ways to implement CDC, they follow the same basic approach: identify changes, order them, and send them downstream.

CDC systems use two main techniques:

• Push-based CDC: Source systems send changes downstream as they occur, often using event-driven triggers. You’ll need to build out what those triggers are and tightly couple source and destination.
• Pull-based CDC: An external tool reads your source system at recurring intervals, looking for information changes. If any are spotted, they’re collated and then logged to your destination. This approach helps to minimize performance impacts and is highly scalable.

ETL and change data capture

CDC directly improves the extract, transform, and load (ETL) process used to migrate data into a data warehouse or data lakehouse. 

Let’s explore what CDC looks like at each stage of the ETL process:

• Extract: CDC identifies change data and extracts data in real time (or near real time). Unlike traditional batch-based extraction, which becomes inefficient as source systems change, CDC continuously captures updates as they happen. This process ensures target tables align without having to refresh source systems entirely every time.
• Transform: CDC avoids large, batch-based transformations. Instead of transforming entire datasets before loading, CDC loads changes continuously and then transforms them in the target repository. An incremental approach lets you keep up with the mass volumes of data that flow through your company.
• Load: Load and transform happen almost simultaneously in CDC. Data changes load into cloud-based targets like warehouses and lakes, where transformations then take place. This approach minimizes latency by removing the need for heavy, pre-load transformations.

Benefits of change data capture

Making CDC part of your data integrations offers a range of benefits — and not just for ETL. CDC’s real-time operations let you integrate, analyze, and use data faster than ever before.

Here are a few ways CDC’s efficiencies can benefit your data integration processes:

• Real-time operations: Without the need for bulk loading or batch windows, ETL can occur in real time. That means better communication between data repositories and sources and timely data for your business. 
• Reduced impact on system resources: By transferring data in tiny increments rather than bulk loads, CDC reduces resource drain.
• Faster database migrations with no downtime: CDC moves data quickly and efficiently, letting you execute real-time database migrations without any downtime. 
• Synchronization across multiple data systems: CDC syncs data to multiple systems to keep everyone on the same page. If you use hybrid environments or time-sensitive applications, continual syncing will help give teams full faith in your data. 

Change data capture methods

There are many methods for implementing change data capture in different types of databases. To keep things straightforward, we’ll focus on relational databases, which are commonly used for operational data processing.

Here are the main change data capture methods.

1. Log-based change data capture

Most databases built from online transaction processing (OLTP) use a transaction log to record changes. As every recoverable change is logged here, it provides a complete and reliable source for CDC.

Log-based CDC asynchronously parses changes from the transaction log, separate from the transactions themselves. The CDC detects any changes to the log and updates other systems accordingly. This form of CDC has minimal impact on performance and enables low-latency content updates.

2. Trigger-based change data capture

Any insert, delete, or update in a trigger-based system causes CDC records to change. Each trigger writes change information in a separate change table. Typically, this system either records the full changed row or records the row’s key and operation type. The former doubles the total amount of data written to a database, while the latter takes up more resources due to needing joins between the change and base tables.

Trigger-based CDC is a legacy strategy that doesn’t see as much use today due to its steep impact on performance.

3. Timestamp-based change data capture

Some applications record row changes in a separate column (e.g., LAST_MODIFIED). You can extract changes by keeping track of when a change most recently occurred. The only problem with this system is that it’s unable to identify deleted rows. If your data requires frequent deletion, you might not have full consistency in your records with timestamp-based CDC.

4. Difference-based change data capture

Difference-based CDC compares two full snapshots of data to identify any changes. Because you have to scan and compare entire systems, this is a demanding form of CDC. It also doesn’t fully preserve a full history of your data, which won’t work for businesses that need exact transactional consistency. 

Change data capture examples and use cases

CDC comes in handy for a number of use cases by making data integration significantly more feasible for real-time scenarios.

Here are a few main change data capture use cases:

• Zero-downtime database migration: Batch migrations typically find an off-peak timeslot to mass migrate content. While this does minimize disruption, even a small operational break might be disastrous in mission-critical workloads. CDC lets you continuously migrate systems iteratively, avoiding the need for downtime or cutovers.
• Real-time fraud detection: Logging incremental changes from your source system lets you monitor behavioral patterns as they emerge. In fraud detection, this allows you to detect and respond to anomalies in real time. Quick reactions can be the difference between stopping a threat before it does damage and a potential breach scenario.
• IoT data integration: IoT systems, like monitors, generate a continual stream of information. CDC lets you capture and transmit these changes efficiently, without overwhelming your resources. This enables a number of use cases, from monitoring warehouse temperature to ensuring factory machinery runs as expected. 

Change data capture tools

CDC tools integrate directly with your existing infrastructure, letting you easily capture and transmit changes in your source databases and systems. 

Here are some common types of data capture tools your business may come across:

• Native CDC tools: Native tools are part of cloud environments or data platforms and work best when ingesting data from their respective ecosystems. For example, Microsoft SQL Server CDC lets you replicate data using change data capture in the SQL server.
• Open-source CDC platforms: Open-source tools like Debezium allow businesses to integrate CDC capabilities into databases for real-time data capture. You’ll typically have to customize and build out open-source platform capabilities to fit your business needs.
• Third-party CDC tools: Third-party platforms like Fivetran deliver fully managed CDC across hundreds of sources and target systems. These take the technical challenge of CDC out of your hands, automating the process and delivering your data with minimal human interaction.

How Fivetran simplifies change data capture

While CDC promises streamlined data integration and better performance, actually integrating it can be a challenge. You’ll have to juggle schema changes, manage log access, and maintain scripts to ensure things run smoothly. Fivetran completely removes that complexity, automating data integration and CDC to support your integration efforts.

Fivetran uses log-based, binary readers to capture data changes directly from source databases in real time. You’ll get high-fidelity, low-latency data replication at scale without needing to query tables or taking performance hits. 

Get started with zero-maintenance pipelines that seamlessly connect to modern data warehouses by requesting a demo today.

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