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Darshan Nagaraj
Head of Data & Analytics
23 Nov, 2022 · 5 min read

Change data capture: An ultimate guide

The idea of working on inaccurate data that’s giving false or misleading information isn’t just frustrating; it negates the point of working with data in the first place.

Data should translate as factual information, but in our fast-paced, always-on world, data has a shelf life. And organizations need to respond to accurate data in real time to stay competitive and take advantage of every business opportunity.

That’s why change data capture (CDC) has become so important as organizations try to keep up with growing data volumes and the speed of change within their environments.

What is change data capture?

CDC is a database mechanism designed to find and track data changes in real time. It’s often used for database replicas or backups. CDC is especially useful given the proliferation of cloud architectures and data warehouses, where there is a constant stream of data and information that requires updating and integrating.

Why is change data capture important?

Traditionally, companies would have to conduct “data batching”, where they transferred datasets in batches once or several times a day. However, this often involved taking the source database offline during “batch windows”, or caused severe latency challenges due to the consumption of additional processing power, which hampered operations and slowed analytics. Unacceptable in today’s 24/7 world.

For example, a payment processing organization might need to check the creditworthiness of new customers. But with batch uploading, any data required to do this would be unavailable, and so the firm would need to outsource this function with fiscal repercussions. Alternatively, it could just be as simple as sales reports not containing the latest sales figures, causing inaccurate financial reporting.

However, with CDC, organizations can transfer data from database transaction logs in small real-time increments as and when changes occur, so the database is updated in real time. For data analysts, this means you can be sure that the data you’re working with is the most accurate up-to-date data available.

This isn’t the only reason CDC is so important. Data is the lifeblood of business, driving sales opportunities, competitive edge, and customer experience. But slow or inefficient access to that data results in inaccurate business decisions, missed opportunities, and inadequate customer experience. Essentially, access to accurate data in real time is no longer a business luxury — it’s a necessity.

Benefits of change data capture

Whether on a business or technical level, the benefits of CDC are compelling:

Faster, more accurate business decisions

Having the capability to conduct real-time analytics with real-time data means business decisions and actions can be taken as and when they’re needed, whether that’s delivering a more personalized digital experience for your customers online or ensuring the right stock is available.

Greater efficiency

CDC is a very efficient way to track changes in a database. With CDC, only data that has changed is synchronized, so fewer resources are required to manage database changes, the replication stream is as small as possible, and the need for batch load updating is eliminated.

Highly reliable

Modern CDC uses the transactional logging functionalities of a database system, so it can reliably track every delete or update that is made, enhancing data accuracy.

Fewer resources required

Without the need to perform batch updating, and because no actual queries are executed and it’s just the changes that are synchronized, using CDC as a replication method is very light on database resources like CPU and memory.

Practical use cases of change data capture

So, now that we know what CDC is, why it’s important, and its major benefits, how can it be applied to your organization? The reason for the growing popularity of CDC is that use cases where CDC can simplify and improve an application and data architecture are many and varied, including:

  • Replication or synchronization of any database table to a data warehouse — particularly useful for odd table setups

  • Cloud migration projects

  • Streaming updates of search indexes

  • Conducting operational/real-time analytics

  • Anomaly or fraud detection

  • Real-time marketing campaign analytics

  • Machine learning and artificial intelligence projects

Change data capture vs. other methods

Of course, as we previously mentioned, there are other methods for achieving backup, replication, and data transfer within the data warehouse environment. In fact, according to the most recent survey, almost 75% of businesses are still using batch processing rather than using CDC.

Performing a full sync, for example, is one alternative to CDC. However, a full database sync is expensive and could introduce additional challenges as the database increases in size, such as additional latency issues.

Setting up a schedule of key-based incremental replication in combination with a full sync (e.g., every week) is also a popular method. This involves replicating data using a reliable replication key, as its name suggests, which is one of the columns in the database table — possibly an integer, timestamp, float, or ID.

However, one of the challenges with this method is that it’s difficult to keep track of any deletes in the source database until the expensive full sync is completed, so you’re temporarily working with outdated data. Also, not every table has an updated column that is suitable for this method of replication.

A good alternative to ensure consistent data is append-only replication. This involves writing logic to allow for append-only tables, where newly replicated data is appended to the end of a table, such as an event store. If no data is ever updated or deleted, an incremental sync on a replication key can be used.

However, this is expensive to set up and maintain from a coding perspective and essentially reproduces what is being done by CDC on the application level.

The future of change data capture

As we’ve discovered, while there are alternative methods still widely used today, the advantages and benefits CDC offers make it a compelling technology. That’s why we use CDC inside the Y42 Modern DataOps Cloud as a replication mechanism for the databases that natively support it.

CDC is a great way to set up your incremental database replication, and while the setup might take a little longer than a normal incremental replication or full sync, the short and long-term benefits far outweigh any initial setup complexities.

The importance of CDC should not be underestimated. As organizations rely more and more on data to help make critical business decisions, CDC ensures this data can be analyzed and integrated faster, ensuring more accurate and accelerated business decisions that drive business growth.

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