What is Change Data Capture (CDC): Best Practices and Examples

Your data is great, until you realize one (or two or a billion) pieces of data need to be changed or updated. If you’ve got data deployed across multiple systems or hundreds of reports, changing the data manually is daunting, if not impossible. But keeping outdated, incomplete, or miscalculated data in those reports will mean you’re not getting the best information for making decisions.

Change data capture (CDC) helps mitigate those problems. It’s a way to keep your data fresh, synchronized, and accurate. The process also creates an efficient way to follow how, why, and when your data is being updated.

What is change data capture (CDC)?

Change data capture is a way to track and capture changes made to data at the source and deploy those changes downstream to areas where that data is being used. It also keeps a record of all the changes made to the data to ensure data integrity. As a result, teams can understand how and why changes were made to any piece of data. This process identifies inserts, updates, and deletes in real time or near real-time, enabling everyone to stay updated with the latest information.

CDC saves time by automatically pushing data updates throughout your system. That means that data stored in a data warehouse can be changed, and those changes are automatically reflected in your business intelligence (BI) or other data analysis tools. This approach is particularly important when you need consistent and synchronized data across different platforms or end uses.

Implementing CDC helps streamline data integration processes. Data is rarely stagnant; it’s often changing, updating, and getting additional context. By capturing only the changes, rather than reprocessing entire data sets, CDC helps your team efficiently get the most accurate and relevant data to end users. This process is important if your company wants to build a reliable, real-time analytics engine, keep everything synchronized across systems, or reduce errors caused by outdated or incomplete data.

CDC has many benefits, so let’s look at what it could mean for your company if you choose to implement change data capture tools, and how it will impact your work.

Importance of change data capture

We touched on this above, but change data capture is crucial. Why does it matter for your business?

The first reason is data integrity. Creating a data-driven organization only works if your teams know they can trust the data they’re using to make decisions. Implementing the tools to use CDC ensures the data stored across your systems remains consistent, reducing discrepancies that could lead to incorrect insights or operational challenges. For example, in e-commerce, where your teams can better respond to market demand with up-to-date inventory data, CDC prevents overselling by ensuring stock information is accurate across platforms. The customer gets a better experience, and your internal teams can keep accurate information on hand.

CDC also affects data quality and accuracy by identifying and addressing changes immediately. Because your data is synchronized in real time, your business can get an instant and clear picture of what’s happening. Let’s consider another example, this time in healthcare. Insurance information can take a long time to process (we call this claims lag), because of this, your team may choose to get a head start on processing the partial data that is available. This can help companies start to get a clear picture of care and its associated costs. However, if your team never receives the full, updated data, you’ll have a problem: the true costs of care will never be accurately recorded, companies cannot accurately forecast future care costs, time may be wasted trying to manually gather information, and patients may have negative financial repercussions. With CDC automatically deploying updated data once it’s available, you can avoid these problems while ensuring you have a wholly accurate view of patient care and healthcare costs. As the additional data from insurance claims is added, dashboards and databases can be updated instantly, reflecting the most recent information.

When your team trusts the data and gets the most recent information faster, they can be more agile in responding to market trends, making decisions based on data they trust, enhancing the customer experience, and optimizing workflows.

How does change data capture work?

Change data capture is important to ensure you get the most accurate and relevant data out of your database and into the data streams where you’re using your data. The process is critical for your business if you’re using data to drive decision-making. Let’s look at exactly what is happening and how it works.

1. First, your CDC tool needs to monitor your source data so it can identify changes and see what and how data is doing within your data lake, data warehouse, or other data storage tools.
2. The next step is monitoring change. A CDC tool can see when data is updated, changed, or deleted. There are different ways tools approach this step; the most common are log-based or trigger-based approaches. We’ll dive into these in more detail below.
3. Your CDC system will capture that change with critical information like the timestamp and what exactly was changed.
4. It then delivers those changes across the systems. It removes data that’s been deleted, adds additional data, or updates numbers depending on the fields changed.
5. Your end tools, like data tables or visualizations, will be processed and refreshed depending on your schedule for updating. Data in your end systems is now automatically synchronized with the data sources.

Methods for change data capture

There are different ways to implement CDC in your data sources. One common approach is called log-based CDC. This is where your CDC tool reads the transaction logs of a database to identify changes. This approach is more efficient for monitoring changes across an entire database, because the tool only processes changes when it identifies them in the logs.

Another approach is called trigger-based CDC. Your team can identify what triggers initiate changes to data downstream. You can set triggers on specific tables for events like inserting data, updating data, or deleting data. This provides accurate and customizable change data capture, but adds progressively more computing power the more data you monitor. It works efficiently on one table, but as you start adding more data or larger tables and need to refactor the entire table, it can add a lot of computer processing power and time to your CDC process.

This table showcases the differences between the two processes:

Choosing the right CDC method for your business depends on your specific use cases. Log-based CDC is ideal for high-volume, real-time data pipelines. On the other hand, trigger-based CDC works well for smaller systems that won’t be impacted as much by performance and computing requirements.

For example, a large e-commerce retailer would likely benefit from a log-based CDC approach. With a large product catalog and a high volume of transactions, the platform needs to track inventory updates, order processing, and customer activity in real time. A log-based approach to CDC efficiently handles these high-volume changes by reading transaction logs. This approach ensures minimal impact on database performance, which is critical for maintaining a seamless shopping experience for customers.

On the other hand, a smaller retail store would likely find a trigger-based approach most effective for its needs. A smaller store has a limited number of products and a straightforward database structure. It might need to accurately track particular events, such as price changes or inventory updates. By setting up triggers on specific tables, the business can capture changes with precision without overwhelming its system resources. Trigger-based CDC provides a simple and customizable solution that fits well within the operational scope of this smaller business.

Another place trigger-based CDC might be more effective is in custom application development. If a software company is developing an application that must react to specific data changes (like user role updates), the development team can set up database triggers to capture these changes, allowing the application to respond in real time. This method offers the developers precise control over the data changes captured, vital for maintaining the application’s functionality and user experience.

Change data capture in ETL (ETL CDC)

Change data capture can impact your ETL processes, so you need to understand how to deploy them in the same data system. ETL stands for Extract, Transform, Load, a process used to move and consolidate data from multiple sources into a centralized data warehouse. This process provides a comprehensive and holistic view of your data using these steps:

• Extract: Data is collected from various sources.
• Transform: Data is cleaned, formatted, and converted to ensure consistency.
• Load: The processed data is stored in the data warehouse for analysis and reporting.

Change data capture integrates into your ETL process by identifying and extracting only the changes (inserts, updates, deletes) since the last ETL run, ensuring you’re only getting the updates rather than reprocessing the entire database. Instead of full data reloads, CDC allows for incremental data extraction, which is faster and less resource-intensive. The benefits of using CDC with ETL include:

• CDC reduces the amount of data transferred, leading to faster load times.
• CDC minimizes the impact on source systems by focusing on specific tables or events, preventing performance degradation.
• Because CDC is more efficient and isn’t processing entire data sets, it is ideal for organizations that need frequent or real-time data updates.
• CDC enhances data accuracy by ensuring only updated or new data is transferred, reducing the risk of errors associated with full data reloads.
• By facilitating incremental updates, CDC allows for more efficient use of storage and resources, leading to cost savings over time.

Implementing change data capture in ETL workflows requires careful planning and execution. Here are some key best practices to ensure success:

• Ensure compatibility. Verify that CDC tools integrate smoothly with your source systems and data warehouse to avoid additional overhead or conversion issues. A seamless integration reduces errors and simplifies ongoing maintenance.
• Perform rigorous testing. Conduct comprehensive testing by simulating various change scenarios. This helps identify potential issues like data loss or duplication early, ensuring the CDC process works reliably in production.
• Monitor and audit performance. Regularly track metrics such as data latency and error rates. Continuous monitoring allows for proactive adjustments, maintaining the efficiency and accuracy of your CDC processes.
• Perform data integrity checks. Implement validation processes to compare source and target data, ensuring consistency and accuracy across systems. Address any discrepancies promptly to maintain trust in your data.
• Establish automated alerts. Automate key aspects of the CDC process to reduce human error and set up alerts for anomalies or failures. Quick notifications allow your team to resolve issues promptly, minimizing downtime.
• Document processes. Keep detailed documentation of your CDC setup and train your team thoroughly. Well-documented processes and informed staff ensure smooth operation and quick troubleshooting when needed.
• Keep scaling in mind. Plan for future growth by choosing scalable CDC tools and configurations. Designing with scalability in mind prevents the need for major reengineering as data volumes increase.

Change data capture examples and use cases

We’ve shared examples of how and why companies would deploy CDC. It benefits companies across verticals and sizes, from large healthcare organizations to smaller retail shops. Here are some additional detailed examples of how and why companies would deploy CDC:

Retail

Let’s revisit and expand on our examples of using CDC for the retail sector. Perhaps you have a national chain of stores that needs to keep its inventory data in sync across hundreds or thousands of locations. As customers make purchases in store or online, CDC helps ensure that inventory levels are updated—in real time—preventing out-of-stock items and keeping customers happy by making sure products are available. This seamless synchronization between sales data and inventory systems enables retailers to manage their supply chains more effectively and respond quickly to demand changes.

Healthcare

The stakes are high for healthcare teams seeking to keep their data accurate and synchronized across systems. Patient records must be updated instantly across various departments and facilities to ensure that healthcare professionals have the most current information at their fingertips. For example, there could be a scenario where a patient is admitted to the emergency room and their records need to reflect the latest test results, medications, and treatment plans for their ER team and primary care providers. This allows teams to deliver consistent and effective care. With CDC, hospitals can maintain accurate and synchronized patient data.

Finance

In the financial industry, CDC can be a valuable tool for maintaining consistency in transaction data across branches and systems. For example, a bank with multiple branches must ensure that account balances are updated in real time to prevent discrepancies that could lead to errors in financial reporting or customer account issues. CDC enables banks to track and synchronize these transactions efficiently, ensuring that customers and financial managers always have accurate information.

Global corporations

Multinational companies that operate in different regions often need to integrate data from various localized databases into a centralized global data warehouse. Without CDC, this process could be cumbersome and prone to delays or inaccuracies. However, by capturing and synchronizing only the changes to the underlying data, these companies can keep their global systems up-to-date, providing a unified view of their operations worldwide.

Microservices

Companies that deploy microservices architecture could benefit from CDC. In this setup, different services within the same company manage their own data. They can use CDC to ensure holistic data is consistent and accurate across these services. For instance, a streaming service might have separate microservices for user profiles, content libraries, and playback history. CDC ensures that any change in one service, like an account modification, is reflected across all relevant services, maintaining a coherent and consistent data state across the platform.

By deploying CDC in your organization, you can effectively manage and improve your complex data systems and data updates. This ensures your team can trust the data they’re using and know they have the most up-to-date information at their fingertips.

While there are many ways to implement CDC, using a tool like Domo can efficiently integrate updated and synchronized data downstream, ensuring everyone has the most accurate information. Because Domo supports the entire lifecycle of data management, it can help ensure that CDC changes are deployed across your entire system, whether you’re doing data visualizations within the tool or sharing embedded data dashboards with others outside of your organization.

Want to get started with your accurate, fully updated data you can trust? Get in touch with Domo today.