What Is Change Data Capture (CDC): Methods, Benefits, and Challenges

What is Change Data Capture?

Change Data Capture (CDC) is a technique used in data management to identify and track changes made to data in a database, and applying those changes to the target system. Because a database is a large collection of data, it becomes difficult to track what data has been added, modified, or deleted. The process looks for changes in the database and records them when found, capturing the modifications in real or near real-time. 

Real-time CDC is more efficient than traditional ETL (extract, transform, load), which would otherwise be resource-intensive and time-consuming. For instance, a database (SQL Server) of an e-commerce website contains information about customers who place orders on the website. Without CDC, periodic updates to the customer information will involve extracting the entire dataset, processing it, and reloading it into the database. 

However, with SQL Server change data capture, the system identifies and extracts the newly added customer information from existing ones in real-time, often employed in data warehouses, where keeping data updated is essential for analytics and reporting. It helps maintain a smooth flow and increases the system’s reliability as there is integration and constant data flow in data warehouses. 

How Change Data Capture Works?

Below is the step-by-step explanation on how change data capture typically works. 

1. Change Identification: The CDC system is set to continuously scan the transaction log to find any changes, such as inserts, updates, or deletes. It looks for information related to what changed and which rows were affected.  
2. Approach: Depending on their use case and requirements, organizations set up different change data capture approaches. Common methods include the log-based approach which involves monitoring the database transaction log to identify changes, and trigger-based CDC where certain triggers are used to capture changes.
3. Capture Relevant Information: Once a change occurs, the CDC system captures the relevant information from the transaction log. This will include the type of change (insert, update, delete), the timestamp (when the change occurred), and the affected rows.
4. Data Storage: The captured data is then stored in a separate repository, tables, or a dedicated CDC database. This is done to ensure the data can be easily analyzed without affecting the performance of the source database.
5. Delivery: After data storage, there may be a need to deliver this information to downstream systems. This could include analytics platforms, data warehouses, or other applications. 

Ongoing monitoring and management of the CDC process is also essential in handling any errors that occur during change capture. Change data capture also maintains a history of changes over time, which is valuable for analytics and auditing purposes.  

The Need for Change Data Capture 

Data Replication

In high-availability cases, maintaining consistent and updated data across different locations is important. This is particularly important in sectors like e-commerce and telecommunication, where uninterrupted access to up-to-date data is essential for customer service and compliance. In such scenarios, data replication is a key strategy to ensure redundant systems have synchronized data. CDC operates in near real-time and plays its role in data replication by continuously identifying changes at the source system. This helps keep data constant and up to date at all locations.  

Audit and Compliance

Organizations must comply with regulatory bodies that mandate them to maintain an audit trail of data changes. Non-compliance can lead to reputational damage and severe penalties. Change data capture is beneficial in this scenario as well. It provides a detailed record of the data’s additions, deletions, and modifications. This helps comply with regulations and serves as a mechanism to detect and rectify any unauthorized changes.  

Cloud Migration

Cloud migration is challenging for most organizations as they have to move large volumes of data from on-premises environments to the cloud. This transition is often done to benefit from the cost-effectiveness, flexibility, and scalability that cloud platforms offer. However, it is challenging in terms of bandwidth, time, and resource utilization. With CDC, you can track and replicate only the changes in data, reducing migration time and bandwidth requirements. This helps in a more efficient and smoother transition to cloud-based infrastructures. 

Different Change Data Capture Methods

Different change data capture methods are used depending on the application’s requirements, such as time-based, log-based, and trigger-based, and a few more. Here, we will go through the important ones only.  

Time-based Change Data Capture

When there’s a more recent timestamp on a row in a database table than the last time data was captured, then it’s considered change. Such a row is typically called ‘LAST_MODIFIED.’ It’s simple to implement as there’s only a need to keep track of when the most recent changes were extracted.  

However, this method cannot track or identify deleted rows. The target systems must also go through each row to find the latest updates. 

Log-based Change Data Capture

Most databases contain transaction logs, also called redo logs, that record all the changes made to the database (Insert, update, and delete). These logs prove useful during crashes for recovery purposes. The transaction logs can propagate changes to the target system without the need to scan operational tables. However, it leads to increased resource usage and storage overhead due to growing transaction logs. Still, log-based CDC is ideal for most occupied databases that cannot afford a lag. 

Trigger-based Change Data Capture

Each time data is inserted, updated, or deleted in a table, a corresponding trigger is activated to record these changes in a separate table. To capture any change in data, one trigger per table is needed. The process also has greater overhead with running triggers on operational tables when the changes are made. The trigger is activated to record changes in a separate table. This approach creates a full version of history, making the data easier to retrieve. 

However, it may affect the database performance as updating the records needs multiple writes.  

Push and Pull Approaches

In the push approach, all processes occur on the source dataset that trigger notifications for changes (insertions, edits, deletions) in real time. The source system pushes the information about the change to the target system. Note that the change in data won’t be noticed if the target system is offline. 

In the pull method, the CDC system actively pulls queries or changes from the source system at scheduled intervals. This puts less load on the source database. Just like the push approach, the pull method also requires an intermediary messenger for offline target systems.  

Change Data Capture in ETL 

ETL is a data integration process that brings updated data from a source system to a target database. The data extraction can be done through data queries and change data capture. Therefore, CDC is an improved version of ETL.  

Here’s what CDC looks like through different stages of ETL: 

Extract: In the past, data extraction involved large batches, causing delays in reflecting source system updates in the target database. Now, with change data capture, data is extracted in real time, capturing only the changes as they occur. This targeted approach significantly reduces the volume of data processed, optimizing the overall ETL process.  

Transform: Transformation is the process of converting a dataset’s structure and format to match the target database. Since the traditional methods involved bulk extraction and transformation, this would take a lot of time. However, in CDC, where transformation remains a key step, data is loaded efficiently and transformed directly into the target repository. This makes CDC approach well-justified with the increasing database sizes.  

Load: This refers to the actual placement of data in the target system. Technically, the transformation and loading occur simultaneously with CDC, making it a more efficient procedure. 

Overcoming Common Change Data Capture Challenges 

Bulk Data Management

Handling the bulk of data requiring extensive changes can pose challenges for the CDC. Its efficiency diminishes notably in such cases. For example, in a cloud collaboration platform, where users continuously edit, create, and share files in real-time, CDC is employed to effectively capture these document changes during a surge in data volume, but during peak usage periods, like simultaneous file edits, it triggers a bulk change in data flow through the CDC pipeline.  

To address the challenge, consider implementing efficient tools such as distributed processing frameworks, and optimizing the deployment strategy like scaling resources dynamically based on usage patterns.  Moreover, enhancing the CDC pipeline and using advanced data processing techniques can help manage simultaneous file edits more effectively.

Schema Changes

Schema changes can disrupt data mapping and synchronization between source and target systems. These changes can complicate CDC, as it needs to adapt to evolving database structures. CDC adaptation can help cater to these changes. Advanced CDC solutions often employ metadata and intelligent algorithms to adjust to schema changes.

Data Integrity

Implementing CDC can make it difficult to maintain data consistency and integrity, especially during complex transformations. The risk of errors may arise due to concurrent changes and potential disruptions due to data mapping.  

This challenge can be easily overcome by validation checks, strong error handling, and reconciliation mechanisms. Moreover, versioning and rollback mechanisms can provide traceability and quick correction, hence maintaining the integrity of transformed data. 

Resource Consumption

CDC consumes substantial system resources that lead to performance issues for concurrent applications. It arises majorly due to inherent demand for disk I/O and CPU memory during data extraction, transformation, and loading. 

A series of optimization strategies can be implemented to address this challenge. For instance, throttling mechanisms can be used to control the rate of data processing. Moreover, fine-tuning parameters such as batch size and parallelism can be employed to align with the system’s capacity.   

Benefits of Change Data Capture 

Large databases call for an efficient data integration system that operates in real–time. Having to change data capture for this purpose presents a number of benefits.  

Let’s see what it is capable of: 

Cloud Migration

Companies are increasingly migrating to cloud storage solutions so they can focus on bringing innovative solutions instead of spending time and effort in maintaining and managing infrastructures. Utilizing CDC in this case ensures data consistency between the on-premises and cloud databases, optimizing the synchronization process and preventing discrepancies.  

Swift Response

CDC has an edge over traditional methods of updating entire datasets. Findings from an IDC study show that 86.5 percent of organizations employ ETL to transfer a minimum of 25 percent of their data. Almost two-thirds (63.9 percent) of data transferred through ETL remains at least five days old when it reaches an analytics database. Such outdated data hinders organizations from delivering the right information when needed and is of no use when dealing with real-time threats. 

CDC is helpful in capturing and analyzing data changes in real–time. With CDC, a cybersecurity firm can detect and respond to security breaches. This enables swift action to prevent the compromise of sensitive information before significant damage occurs. 

Microservices Architecture

Data needs to be transferred from source datasets to multiple destination systems. It becomes easier using CDC as it helps maintain synchronization between source and target datasets during the process. Taking the real-time data transfer approach, it’s now more convenient to synchronize multiple data systems no matter where they are.  

Less Pressure on Operational Databases

Operational datasets have to monitor employee analytics and other activities continuously. CDC works by alleviating pressure on operational databases by optimizing the identification process and transferring data changes. In traditional synchronization methods, there’s a considerable load on operational databases, but a targeted approach using CDC minimizes this strain. Instead of transferring whole datasets, the CDC captures and transmits only specific updates that have occurred since the last synchronization. 

Faster Data Processing

Product differentiation and quality services do not remain the only success criteria for businesses. The need of the hour is efficient data processes including the collection, storage, and data usage. Given the contemporary demand for fast data processing, CDC’s capabilities make it an essential component for businesses. 

Maintaining Competitive Advantage

While the importance of an efficient system in data processes is evident, about 75% of businesses still rely on batch processing to modify datasets. Employing change data capture can be an effective way to address these challenges. And since about 80% of companies plan to implement multi-cloud storage strategies in the coming years, the need for a good change data capture mechanism becomes increasingly paramount. 

Furthermore, CDC empowers business agility by letting enterprises stay updated with their data as it facilitates replication across various cloud environments. This benefits those companies dealing with diverse cloud solutions, whether a combination of public, private, or hybrid clouds. 

Change Data Capture with Astera 

To ensure the business thrives for the years to come, decisions should be based on real-time data. And to synchronize all the databases within an organization for a coherent approach, change data capture is one of the potential solutions as it minimizes disruption during data transfer and reduces costs.  

Astera’s Data Pipeline Builder stands out as a key player in data management, offering comprehensive features for designing, implementing, and managing data integration processes. It supports various change data capture methods for different databases, including time-based, log-based, and trigger-based mechanisms.  

Experience the advantages of change data capture with our 14-day free trial. Sign up today to explore the functionality and benefits firsthand.