CDC Replication: How It Works, Examples, Best Practices

Modern businesses rely on timely, reliable data to make informed decisions and maintain a competitive edge. Change data capture (CDC) replication allows organizations to meet this demand by identifying and replicating only the data that has changed, reducing strain on systems and ensuring up-to-date information flows across platforms. 

In this blog, we’ll explore how CDC replication works, highlight some real-world use cases, and share best practices that can help you get the most value from your data replication strategy.

What is CDC replication? 

Change data capture is a data management technique to identify and track changes made to data in a source system; it then makes sure those changes are captured and delivered to systems further downstream in near real time. 

Rather than requiring full data loads or time-consuming comparisons between data sets, CDC monitors for inserts, updates, and deletes in the source database and transmits only those changes. This approach helps maintain data accuracy and freshness across systems, especially in environments where timely access to updated data is critical, such as analytics platforms, data warehouses, or real-time reporting tools.

In modern data architectures, CDC is essential to making available services like streaming analytics, as well as supporting event-driven applications and reducing data replication overhead. By capturing only what has changed, CDC helps organizations achieve more efficient data pipelines, minimize latency, and ensure consistency between source systems and integrated data platforms.

How does CDC replication work?  

Depending on the setup of your organization and how you’re using it, CDC can look different. Here’s an overview of the general process as well as some of the most common methods. 

CDC process

Change data capture (CDC) works by identifying and delivering only the data that has changed in a source system, rather than replicating entire data sets. The process typically follows a sequence of steps to ensure data changes are captured accurately and transferred efficiently to downstream systems like data warehouses, lakes, or analytics tools. 

Here’s how the CDC process generally works:

1. Monitoring the source system. CDC monitors the source database or application for changes such as inserts, updates, and deletes.
2. Identifying changes. Once changes occur, CDC detects them through one of several methods (described in the “CDC methods” section below), depending on the system's capabilities and setup.
3. Capturing changes. The changes are then captured into a staging area or intermediate format for processing.
4. Transforming and formatting (optional). If needed, changes are transformed into a format that suits the destination system’s schema or requirements.
5. Delivering to the destination. The processed changes are then sent to the target system in real time or in batches, ensuring up-to-date, consistent data.

CDC methods

There are several ways CDC can be implemented, depending on the architecture and technology stack. Each of these methods provides a unique balance of simplicity, performance, and precision. Organizations should consider their existing infrastructure, performance needs, and data latency requirements when selecting the most appropriate CDC method.

Here are some of the most common CDC methods. 

Log-based CDC

This approach taps directly into a database’s transaction logs, the low-level records that capture every change made to the data. By reading from these logs, log-based CDC can detect inserts, updates, and deletes without having to query the data tables. 

This method is highly efficient and minimizes the performance impact on the source system, making it ideal for high-volume environments. It also offers high fidelity, preserving the exact sequence and nature of changes, which is especially valuable for downstream replication or real-time analytics.

Trigger-based CDC

Trigger-based CDC uses database triggers—automated procedures that execute in response to data changes. Whenever a row is inserted, updated, or deleted, the corresponding trigger writes information about that change into a tracking table. 

This makes it easy to isolate and process specific data modifications. While this approach is useful for systems that lack access to transaction logs, it can introduce significant overhead, especially on high-write systems. Performance tuning and careful design are critical to avoid bottlenecks.

Timestamp-based CDC

With this method, each table includes a last_modified or updated_at column. The CDC process identifies changes by comparing these timestamps against the last time data was extracted. This is a straightforward and lightweight solution that requires minimal infrastructure changes. 

However, it has limitations. It typically can’t capture delete operations, and it depends heavily on the integrity and consistency of timestamp data. Still, it’s a good fit for applications with predictable update patterns or where real-time accuracy is less critical.

Snapshot-based CDC

Snapshot-based CDC captures the entire state of a table at regular intervals and compares it to previous snapshots to detect changes. This method doesn’t rely on logs, timestamps, or triggers, which makes it flexible across different systems and data sources. 

However, it’s resource-intensive, especially for large data sets, and not suitable for real-time use cases. Because it involves scanning and comparing entire tables, it’s often reserved for systems where other CDC methods aren’t feasible or when changes occur infrequently.

Audit column-based CDC

This technique involves modifying the source schema to include audit fields such as created_at, updated_at, deleted_at, and operation_type. These fields give precise visibility into when a change occurred and what kind of change it was. Audit column CDC is relatively easy to implement and allows for highly targeted data extraction. 

However, it requires upfront schema changes and consistent enforcement of audit practices across teams and applications. It’s particularly valuable for compliance reporting, auditing, and long-term change tracking.

Table delta comparison

In delta-based CDC, data snapshots (or hashes) are compared across two versions of a table to identify changes. This method doesn’t depend on logs or schema modifications, making it a flexible option in environments where those aren’t available. 

However, comparing entire tables—especially large ones—can cost a lot of computational power and be time-consuming. It may also be less precise in identifying the nature of the change, such as insert versus update. Still, it can be effective in batch-oriented workflows or during data migrations.

Benefits of CDC replication

Enterprises looking to modernize their data architecture and improve real-time decision-making are increasingly turning to CDC replication. This approach offers a range of advantages that streamline data workflows, support analytics initiatives, and enhance overall operational efficiency. 

With CDC replication, businesses can discover valuable infomration faster, create more responsive applications, and improve data operations, making it an essential capability for modern data ecosystems. 

Here are some of the key benefits of CDC replication:

Real-time data availability

CDC replication enables near-instantaneous data updates across systems by capturing and propagating changes as they occur. This supports real-time analytics, dashboards, and decision-making without the need to constantly reload entire data sets.

Reduced system load

Because CDC only captures and replicates incremental changes instead of full data sets, it significantly reduces the strain on source databases. This lowers compute and I/O costs and minimizes performance impact on transactional systems.

Improved data accuracy and consistency

CDC ensures that target systems like data warehouses, data lakes, or analytics platforms mirror the source data with high fidelity. This consistency is crucial for accurate reporting, compliance, and syncing across distributed systems.

Faster data integration

By continuously replicating changes, CDC accelerates data integration processes. Enterprises can move and consolidate data across platforms more efficiently, which is especially useful for hybrid or multi-cloud environments.

Better support for modern architectures

CDC replication is a core component of event-driven and microservices architectures, enabling loosely coupled systems to respond to data changes in real time. It also facilitates streaming pipelines and supports tools like Kafka or cloud-native data platforms.

Enhanced scalability

As enterprises grow, their data needs become more complex. CDC replication scales well with high-volume environments, allowing organizations to keep systems synchronized without reengineering core infrastructure.

Examples of CDC replication 

CDC replication is a powerful tool for organizations to track and transfer real-time changes from their databases to downstream systems. By doing so, data can remain consistent and current across platforms, which is critical in fast-moving business environments. CDC is especially valuable for supporting analytics, minimizing data latency, and reducing the processing burden on source systems. 

Below are several real-world use cases that highlight how enterprises are leveraging CDC replication across various industries.

Real-time analytics and dashboards

Organizations use CDC to feed real-time data into analytics platforms and dashboards, enabling immediate visibility into operational metrics. For instance, retail companies can track sales transactions as they happen and instantly update their business intelligence tools, allowing them to respond quickly to inventory changes, customer behavior, or emerging trends.

Data lake synchronization

Enterprises often replicate data from operational databases to data lakes using CDC to maintain fresh, queryable data without taxing production systems. This supports advanced data science, machine learning, and long-term storage strategies, especially in industries like healthcare or finance, where historical and real-time data have to coexist for in-depth analysis.

Cloud migration and hybrid architectures

As organizations shift from on-premises systems to the cloud, CDC replication plays a critical role in synchronizing data across environments. It ensures that cloud databases stay in sync with legacy systems, minimizing downtime and supporting a phased migration. This is especially useful in manufacturing or telecom, where business continuity is essential.

Fraud detection and compliance monitoring

In highly regulated sectors like banking and insurance, CDC is used to detect anomalies in transactional data streams. By capturing and analyzing changes in real time, companies can flag suspicious activity and meet regulatory requirements more effectively, reducing risks and enhancing oversight.

CRM and ERP integration

Businesses often integrate customer and operational data across platforms like CRMs and ERPs using CDC. This ensures that changes in one system, such as a new customer address or updated payment status, are reflected across the organization, improving coordination and reducing data entry errors.

Supply chain and logistics optimization

CDC enables supply chain managers to monitor changes in inventory, shipments, and vendor data in real time. For example, a logistics company can use CDC to update tracking information across platforms and notify stakeholders the moment a shipment status changes, enhancing responsiveness and reliability.

IoT and sensor data processing

Industries with high volumes of machine or sensor data, like energy or agriculture, use CDC to replicate time-series changes to downstream analytics engines. This allows for continuous monitoring, predictive maintenance, and efficient resource management based on real-time conditions.

Supporting machine learning pipelines with up-to-date data

Machine learning models are only as good as the data they’re trained and updated on. CDC replication ensures that changes from operational systems flow continuously into data lakes or ML feature stores, allowing organizations to keep models current without full data reloads. This is especially valuable for businesses running near-real-time predictions, such as fraud detection in banking or dynamic pricing in retail, where latency in model updates can lead to costly decisions.

CDC replication best practices 

Reliable and real-time data replication is essential for modern analytics and business agility, and CDC plays a key role in making this possible. However, to ensure successful implementation and maximum value, organizations should follow a set of best practices. These guidelines help reduce latency, maintain data integrity, and align with evolving data architectures while ensuring scalability and security.

Choose the right CDC method for your use case

Not all CDC methods are equally suited for every environment. Log-based CDC is typically the most efficient for high-throughput scenarios, while trigger-based or query-based methods might be simpler for smaller data sets or systems with limited access to logs. Evaluate your data source capabilities, latency requirements, and operational overhead before selecting a method.

Ensure schema and metadata consistency

A robust CDC strategy must include monitoring for schema changes, such as new columns or altered data types. Without schema synchronization, downstream systems can break or produce inaccurate results. Use tools that support schema evolution or implement validation layers that keep schemas aligned between source and target systems.

Implement robust error handling and monitoring

CDC pipelines require consistent oversight to detect failed data loads, missing transactions, or performance bottlenecks. Implement logging, alerting, and recovery mechanisms so issues can be resolved quickly. Monitoring also helps with capacity planning and compliance reporting.

Optimize network and system performance

Real-time CDC processes can put strain on your network and compute resources if not properly managed. Use compression, batching, or incremental loads where possible. Balance throughput with resource usage to maintain system health without slowing down production environments.

Secure data in transit and at rest

Since CDC deals with the constant movement of potentially sensitive data, it’s vital to apply encryption and access controls. Protect data pipelines from unauthorized access and ensure compliance with privacy regulations such as GDPR, HIPAA, or CCPA by anonymizing or masking data when needed.

Test thoroughly before going live

CDC replication can introduce hard-to-trace data quality issues if not properly validated. Run simulations or test environments to verify that change detection, transformation logic, and synchronization work as expected. Validate edge cases such as deletes, null values, and out-of-order transactions.

Plan for scalability and future growth

As data volumes and business needs evolve, your CDC setup should be flexible enough to scale. Use modular architectures and cloud-native tools that allow you to scale compute, storage, and processing independently. Future-proofing ensures your data integration continues to perform as demands grow.

Document data lineage and transformations

Maintain transparency in how and where changes are captured, transformed, and delivered. Clear data lineage supports governance, debugging, and auditing. It also enables business users to trust the data they see in analytics and reporting platforms.

Test and validate CDC processes regularly

Even after implementation, CDC workflows require consistent testing and validation to ensure accuracy and reliability. Data environments evolve—new tables may be added, schemas may change, or source systems may behave differently. Regular testing helps catch mismatches, data drift, or unintended replication gaps before they become business-critical issues. Incorporating automated data validation into your CDC pipeline can significantly reduce manual overhead and ensure ongoing data integrity.

Turn CDC into a competitive advantage with Domo

CDC replication plays a vital role in helping enterprises move data efficiently, minimize latency, and ensure consistency across systems without the overhead of replicating entire data sets. 

With Domo’s robust support for change data capture, businesses can integrate real-time data from a wide variety of sources, streamlining workflows and powering up-to-the-minute insights across their organizations. 

To learn more about how Domo makes CDC easy, reliable, and scalable, explore our integration capabilities here.