What is Change Data Capture (CDC)? Tools and examples (2023)

Data is growing everywhere. Experts predict that in 2025the global amount of data will amount to 181 zettabytes, or more than four times 2019 pre-Covid levels. Data is inevitable in every aspect of life – and this is doubly true in business. In a world changed by COVID, the business world is a world of data.

But it can seem like for every problem that data solves, another emerges: saturated and isolated data streams make it difficult to make meaningful connections between data sets. New data gives us new opportunities to solve problems, while maintaining the freshness, quality and relevance of the datamy dateidatawarehousesis an endless effort. And despite the proliferation of machine learning and automated solutions, much of our data analysis is still the product of inefficient, mundane, and manual tasks.

When you add it all up, organizations need to extract the most value from their data, and they need to do it in the most scalable way possible. To support this goal, data integrators and engineers need a real-time data replication solution that helps them avoid data loss and ensure data is current in all use cases. and multi-cloud environments and increase the agility of your business.

Change Data Capture (CDC) allows data to be quickly replicated from source applications to any destination, without the tedious engineering work of extracting or replicating entire data sets. This ensures that organizations always have access to the latest, most up-to-date data.

Change the data logging definition

Change commit refers to the process of identifying and committing changes as they are made to a database or native application, then propagating those changes in real time to a downstream process, system, or data lake.

This advanced data replication and loading technology reduces the time and resource costs of data warehousing programs and enables real-time data integration across the enterprise. By detecting changed records in data sources in real time and communicating those changes to the ETL data warehouse, change data capture can dramatically reduce the need for bulk warehouse updates.

CDC is increasingly the most popular form of data replication because it only sends the most relevant data, reducing the burden on the system. And because CDC only imports data that has changed -- rather than replicating entire databases -- CDC can dramatically speed up data processing and enable real-time analytics.

What is data replication and why is it important?

Data replication is exactly what it sounds like: the process of simultaneously making copies and storing the same data in multiple locations. Establishing this kind of redundancy for your database systems offers a wide range of benefits while improving data availability and accessibility, as well as system resiliency and reliability.

Data replication ensures that you always have an accurate backup in the event of a disaster, hardware failure or system breach. And having a local copy of important datasets can reduce latency and lag when global teams work from the same source data, for example in Asia and North America.

When it comes to data analytics, there is another layer for data replication. Data-driven organizations often replicate data from multiple sources into data warehouses, where they use it to power business intelligence (BI) tools.

However, like any system with redundancy, data replication can have its drawbacks. When there are updates to data stored in multiple locations, they should be updated system-wide to avoid conflicts and confusion. This can double (or triple) the growth of data management over time and puts a strain on resources, forcing data integrators and engineers to monitor multiple systems and databases or periodically replicate an entire database from source systems to all other systems, applications . and data lakes or data warehouses that use the same data sets.

CDC mitigates this increase by replicating only new data or data that has recently changed, giving users all the benefits of data replication without the drawbacks.

How does the GGD work?

With new data coming in all the time and existing data constantly changing, data replication becomes more and more complicated. Because it works continuously rather than sending updates in bulk, CDC provides organizations with faster updates and more efficient scaling as more data becomes available for analysis.

However, not all CDC implementations are identical - or provide identical benefits. Let's take a look at the three CDC methods and explore the benefits and challenges of each:

Script-based CDC

It is possible to build a CDC solution on an application by writing a SQL-level script that monitors only key fields within the database. When there is a change to that field (or fields) in the source table, it serves as an indicator that the row has changed. The modified rows can then be replicated to the destination in real time, or they can be replicated asynchronously during a scheduled bulk load.

The script-based method is quite simple, but creating and maintaining a script can be challenging, especially in a rapidly changing or constantly changing data environment. For an effective script, it may be necessary to change the schema, such as adding a date/time field to indicate when a record was created or updated, adding a version number to log files, or enabling a Boolean status indicator.

Since the script only looks at selected fields, data integrity can be an issue if there are changes to the table schema. And while CDC still requires fewer resources than many other replication methods, by retrieving data from the source database, CDC can add additional load to the system.

CDC based on triggers

Instead of writing an application level script, another CDC solution looks for database triggers. Triggers are functions written in software to record changes based on certain events or "triggers". Most triggers fire when there is a change to the source table, using SQL syntax such as "BEFORE UPDATE" or "AFTER INSERT".

This method gives developers control because they can define triggers to commit changes and then generate a change log. And because triggers are reliable and specific, data changes can be recorded in near real-time.

However, there are some drawbacks to the approach. The first is obvious: since triggers must be defined for each table, downstream problems can occur when the tables are replicated. The reliability of this solution can also be compromised if, for example, users can intentionally disable triggers or enable certain operations.

In addition, with each transaction, a change record is created in a separate table, as well as in the database's transaction log file. Because it has to intermittently go to the source database, trigger-based CDC adds additional load to the system and can negatively impact latency.

Log-based CDC

The most efficient and effective CDC method is based on an existing feature of corporate databases: the transaction log. In a typical corporate database, all data changes are tracked in a transaction log. In the event of a disaster or system crash, data can be reconstructed from these transaction logs.

CDC's log-based solution monitors the transaction log for changes. When those changes happen, they are pushed to the destination data store in real time.

Because there are transaction logs to ensure consistency, log-based CDC is extremely reliable and records every change. And since the transaction logs exist separately from the database records, there's no need to write additional procedures that put extra strain on the system, meaning the process doesn't affect the original database's transaction performance. Best of all, continuous log-based CDC operates with extremely low latency, monitoring changes in the transaction log and sending those changes to the destination or target system in real time.

But because log-based CDC takes advantage of the transaction log, it is also subject to the limitations of that log file - and log formats are often proprietary. As a result, log-based CDC only works with databases that support log-based CDC. However, given all the advantages in terms of reliability, speed and price, this is a small disadvantage.

CDC and ETL

Today, the average organization draws on more than400 data sources. When you rely on so many different sources, the data you get has to be in different formats or rules. Moving as is from a data source to a target system via simple APIs or connectors would likely lead to duplication, confusion, and other data errors.

ETL — which stands for Extract, Transform, Load— is a key technology for bringing data from multiple disparate data sources into one centralized location. As the name implies, this technology takes data from a source, transforms it according to an organization's standards and norms, and then loads it into a data lake or data warehouse, such as Redshift, Azure, or BigQuery.

Without ETL, it would be nearly impossible to turn massive amounts of data into actionable business intelligence. But when the process relies on bulk-loading the entire source database into the target system, it consumes a lot of system resources, making ETL impractical at times - especially for large datasets.

That's where the CDC comes in. Because the CDC process uses only the latest, newest, most recently changed data, the ETL system takes a lot of the pressure off. Essentially, CDC optimizes the ETL process.

At the same time, ETL can compensate for the primary weakness of log-based CDC. Unlike CDC, ETL is not limited by proprietary log formats. This means it can replicate data from any source, including data that cannot be replicated through log-based CDC.
In short, CDC and ETL are complementary technologies: CDC makes ETL more efficient and ETL captures all data sources that log-based CDC cannot capture.

Use cases of CDC technology

Because CDC gives organizations real-time access to the latest data, the applications are almost endless. With change data collection technology like Talend CDC, organizations can address some of their most pressing challenges:

Get the right data in the right hands in the right formats

It's not enough to just have data - that data needs to be accessible. CDC makes it easy to create, manage, and maintain data pipelines for use across an organization. This means all users have access to the most up-to-date and accurate data for business intelligence, reporting and direct use in analytics and applications.

Increase the accuracy, quality and reliability of data

CDC's real-time, low-touch replication of data removes the most common barriers to reliable data. A data lake or data warehouse is guaranteed to always have the latest, most relevant data. This allows users to be more confident in their analytics and data-driven decisions.

Improve regulatory compliance and adherence to privacy standards

Compliance with regulatory standards isn't as easy as it sounds: When an organization receives a request to remove personal information from its databases, the first step is to locate that information. If a claimant has multiple related logs across multiple applications, for example, web forms, CRM, and in-product activity logs, compliance can be a challenge.

By keeping the data current and consistent, CDC makes it easy to find and manage that data, protecting both the business and the consumer.

Extensive business data integration

Talend CDC helps reach customershealth dataEnabling data teams to robustly and securely replicate data to increase data reliability and accuracy. Our proven enterprise-grade replication capabilities help businesses avoid data loss, ensure data freshness, and achieve desired business outcomes.

Talend's change data capture feature works with a wide variety of source databases.

Talend data integrationprovides end-to-end support for all aspects of data integration and management in a single platform. With an intuitive development environment, users can easily design, develop and implement processes for database conversion, data warehouse loading, real-time data synchronization or any other integration project.

In addition to advanced runtime features such as change data capture, Talend's data warehousing tools include support for advanced ETL testing, with features such as context management and remote task execution. The system also offers enterprise-class functionality such as workflow collaboration tools, real-time load balancing, and support for innovative mass storage technologies such as Hadoop.

In addition to our superior functionality, Talend offers professional technical support from Talend data integration experts. For organizations launching master data management initiatives, Talend also offersMDM solutionthat integrates seamlessly with Talenda.

Learn more about Talentdata integration solutionstoday and take advantage of the leading open source data integration tool.