Change Data Capture (CDC): What it is, importance, and examples

Change Data Capture (CDC) extracts data changes in a source database and ingests those changes into cloud storage in near-real time. CDC is more efficient and faster than batch data ingestion, making it the go-to solution for data teams and analysts who need to get data into the cloud and analyze it quickly. 

There are many methods of implementing CDC. Use cases like fraud detection, real-time marketing campaigns, operational analytics, AI/ML modeling, and more not only need data quickly but also need to capture every data change for complete analytics. Choosing the CDC method for your use case is a strategic decision that needs to be made before researching vendor solutions.

What is Change Data Capture (CDC)?

Change Data Capture (CDC) is a software design pattern that tracks and manages various incremental changes commonly found inside different data sources. These data sources can typically consist of operational and OLTP databases, guaranteeing that the data integrity and consistency are valid throughout different systems and deployment environments. With the ability to capture changes as they occur, the CDC can provide real-time data replication and transfer. It allows organizations to use the best tools for each task, such as moving data from a legacy database to a significantly more modern data platform. A few examples of modern data platforms include document databases, search databases, and cloud data warehouses.

Minimizing resource usage is possible when using CDC technology, but it is not easily doable when using full data replication. Full data replication is a simple but inefficient way to propagate changes, which can cause performance problems with the data source and clog the overall transport system. CDC is significantly more efficient, allowing data consumers to absorb real-time changes, leading to instant and valuable analytics. This makes CDC technology incredibly useful for organizations that need continuous operations with no downtime when using batch processing methods. CDC can provide a company with more than one advantage. With the CDC’s ability to send data in real-time and when changes happen, businesses can easily use real-time access and operational data for competitive decision and task management. Some of the CDC’s most prominent features include real-time analytics, data streaming, and machine learning. These features guarantee that the CDC is a much-required tool for modern data and management processing. 

Why is Change Data Capture important?

As databases grow exponentially over time, performing large, batch-based operations can become inefficient, slow, and resource intensive. These large batch extractions can put a strain on the performance of the source database itself, potentially impacting write performance of new data records. 

Monitoring and extracting changes as they occur with CDC simplifies the replication process, is incredibly efficient, and consumes fewer compute resources in the database so there is minimal, if any, performance impact. CDC also maintains timely consistency of datasets across all downstream data stores and processing that rely on this data. The incremental change data can be loaded in microbatches–for example, every minute–instead of waiting on large batch jobs that may run only once a day or every few hours.

How Does Change Data Capture Work?

CDC is a technology that can be used to track and propagate changes. When using a relational database, it is expected to take actions as you use and modify the data inside a database. Typically, we could use actionable methods (INSERT, UPDATE, DELETE) to change the data stored inside a database. Relational databases like MySQL, PostgreSQL, Microsoft SQL, and Oracle can use these actions for tracking and propagation, which can be used for downstream systems such as caches, search indexing, data warehouses, or even data lakes. When using this strategy, it is guaranteed that the target system will be continuously updated with the most recent data. This is crucial to ensuring the real-time analytics and system work efficiently. 

There are two primary approaches to implementing CDC: push and pull.

Push Method: The source database actively updates the target systems with the push method. This approach has the advantage of ensuring that the target system receives data changes at close to real-time speeds. One thing to note is that if the target systems are unavailable or offline, then changes to data could be lost. To prevent such a scenario from occurring, a messaging system capable of temporarily storing changes until they can be successfully sent to the target system can be used.

Pull Method: When using the pull method, the source database can log changes, and the target system can consistently poll the source database to retrieve any changes. The advantage of using this approach is that it lightens the load on the source database, although a drawback is that it introduces a lag between data changes and their reflection in the target systems. Similarly to the push method, it's possible to utilize a messaging system that can be used to guarantee that changes are not lost when the target systems are not accessible.

CDC can utilize various technologies to capture and replicate data changes:

• Timestamps: Capable of tracking changes using “LAST_UPDATED” and “DATE_MODIFIED” columns, although it uses significantly high CPU resources for scanning data tables
• Table Differencing: Can compare source and target tables to load only differing data, that is comprehensive but CPU-intensive
• Triggers: Makes use of database triggers for logging changes, which can place a strain on the system because each table needs to have a trigger
• Log-Based: Scans the database logs for any changes and can capture updates with additional SQL load, reducing CPU stress.

CDC has several advantages over traditional batch processing methods, which suffer from replicating data at specific intervals, which can slow production and lead to delays. With CDC, you gain access to real-time or near-real-time data synchronization that can eliminate the need for bug bulk transfers. Incorporating CDC can easily save time and resources. When we look at practical and production-ready applications, CDC usually replicates data between operational databases and cloud-based data warehouses like Google BigQuery, Snowflake, Amazon Redshift, and Microsoft Azure. There are many benefits to using this setup because it gives teams the capability to perform data analysis without having to strain the production database. This is a requirement in a business that is operating 24/7 and has to have an uptime of close to 100%, as production slowdowns can cause problems for the company, clients, and customers, which are unacceptable. 

The benefits offered by the CDC make it a worthwhile investment. CDC's ability to transform data management by ensuring that the source and target databases are always working and in sync is excellent. The technology supports real-time data replication, can minimize resource usage, and eliminates delays, a common problem with traditional batch processing methods. This means that it is an essential tool for all modern data-driven operations.

Change Data Capture (CDC) is a key component in modern data architectures, enabling organizations to track and capture changes in database tables. There are several methods available for CDC, each with its advantages and limitations. The right choice of method depends on factors such as the database technology, use case, and performance requirements.

Below is an expanded overview of common CDC methods.

1. Timestamps on Rows

How it works:
This method relies on a timestamp column in the database, where each row is marked with a time value representing the last update. The timestamp is compared to the last capture time to identify changed data.

Pros:

• Easy to implement: Simply adding a timestamp column to each table makes this approach simple to understand and deploy.
• Real-time updates: Changes are captured as soon as they occur when the timestamp is updated, enabling real-time or near-real-time updates.
• Minimal overhead: No need for complex logic—just a timestamp comparison is required.

Cons:

• Synchronization challenges: If there are distributed systems, maintaining synchronization of timestamps across all systems can be challenging.
• Precision issues: Timestamps might not have enough precision, meaning simultaneous changes might not be captured.
• Performance impact: Updating timestamps on each row can introduce performance overhead, especially for high-volume tables.

Best for:
Batch processing environments or analytical use cases where low latency is not a strict requirement.

2. Version Numbers on Rows

How it works:
A version number column is added to each row. Each time a row is updated, its version number is incremented. The version number is used to identify changed rows.

Pros:

• Clear sequence of changes: Versioning provides a simple way to track the sequence of changes over time, allowing for clear identification of when and how data changed.
• Optimistic concurrency control: Version numbers can help handle concurrent updates without conflict.
• Easy logic comparison: It’s straightforward to compare the version number to detect updates.

Cons:

• Additional column required: You need to add a new column for version numbers, and this must be managed correctly for each update.
• Concurrency conflicts: Concurrent updates can cause version conflicts, where two updates to the same row might not be captured correctly.
• Manual tracking: The versioning system must be carefully maintained to ensure that updates are tracked accurately.

Best for:
Environments where tracking a sequence of changes is important, such as for historical record-keeping or auditing purposes.

3. Status Indicators on Rows

How it works:
This method uses a status indicator (often a Boolean flag, such as is_changed) to mark whether a row has changed. Changes are identified based on the indicator's value (TRUE or FALSE).

Pros:

• Simple to implement: A Boolean column indicating changes is straightforward to add and requires minimal logic.
• Low performance overhead: Boolean values are lightweight and don’t require significant system resources.
• Easy identification: It’s easy to flag rows as changed or unchanged with minimal processing.

Cons:

• Lack of granularity: While you know that changes have occurred, you don’t get details on the type of change or the sequence of updates.
• Reset complexity: After changes are processed, the status indicators need to be reset, which can lead to errors if done prematurely.
• Limited insight: This method doesn’t provide full historical tracking, only whether a change has occurred or not.

Best for:
Low-complexity environments or when only the detection of changes (not details) is required.

4. Time/Version/Status Combination

How it works:
A combination of timestamp, version number, and status indicator columns can be used together to capture changes in a comprehensive way.

Pros:

• Comprehensive tracking: By combining multiple CDC methods, you can capture a more complete history of data changes.
• Flexible: The combination of methods can be adapted to different scenarios or requirements.
• Thorough tracking: Multiple attributes (e.g., timestamp, version number, status) provide a detailed record of changes, enabling more accurate change detection and more reliable data integrity.

Cons:

• Increased complexity: Adding multiple columns for tracking changes increases the complexity of the database schema and logic.
• Higher storage requirements: More columns mean more data to store and process, which can increase storage requirements and processing overhead.
• Performance impact: With multiple fields being updated, there could be a performance impact, particularly in high-volume systems.

Best for:
Complex data environments requiring detailed tracking of changes, such as large-scale enterprise applications or systems that require high levels of data accuracy and integrity.

5. Trigger-Based CDC

How it works:
Triggers are database objects that automatically execute a function when certain events (INSERT, UPDATE, DELETE) occur. For CDC, triggers can be used to log changes into a separate change table, which can later be processed.

Pros:

• Real-time capture: Changes are captured as they happen, with minimal delay, allowing for near-real-time updates.
• Granular control: You can specify exactly which operations (insert, update, delete) and even which tables or columns should be tracked.
• No schema modifications required: Unlike other methods, triggers don’t require changes to the data schema itself.

Cons:

• Performance overhead: Triggers add processing time to each transaction, especially if they are logging many changes.
• Scalability concerns: In high-volume environments, managing triggers and their associated overhead can become challenging.
• Error handling complexity: Writing complex logic for triggers and managing errors can be tricky.

Best for:
Smaller systems or when only specific tables or operations need to be tracked. Useful when real-time capture of changes is required.

6. Log-Based CDC

How it works:
Log-based CDC works by reading directly from the database’s transaction logs (e.g., MySQL binlog, PostgreSQL Write-Ahead Log (WAL), Oracle redo logs). These logs record every transaction that alters the database, and CDC tools read from these logs to detect changes.

Pros:

• Non-intrusive: This method does not interfere with the regular operation of applications or databases.
• Low latency and high accuracy: Log-based CDC captures changes almost immediately after they occur, with minimal delay.
• Supports complex operations: Captures inserts, updates, deletes, and schema changes (such as DDL operations).

Cons:

• Requires database access: This method requires direct access to database logs, which may be restricted in managed databases or cloud environments.
• Configuration complexity: Setting up log-based CDC can be complex, especially in ensuring proper access to transaction logs and handling edge cases.
• Database-specific: This method is highly dependent on the database system and may require custom setup for different database types.

Best for:
High-volume transactional systems where near-real-time replication is necessary, especially when working with complex changes such as schema updates or deletes.

7. Diff-Based (Snapshot Comparison) CDC

How it works:
Diff-based CDC captures changes by taking periodic full snapshots of the entire table (or specific columns) and comparing them to previous snapshots to detect changes.

Pros:

• Conceptually simple: The approach is straightforward, as it compares the entire table or dataset to identify differences.
• No special database features required: Unlike other methods, diff-based CDC doesn’t require specific features like triggers or timestamps—just full table comparisons.

Cons:

• Inefficient for large datasets: Comparing entire tables, especially large ones, can be very resource-intensive.
• High storage and compute cost: Storing multiple snapshots and performing regular comparisons incurs significant storage and compute overhead.
• Not suitable for frequent updates: As it requires full table scans, this method doesn’t scale well for environments with frequent updates.

Best for:
Small datasets or legacy systems lacking built-in CDC support, or environments where full table comparisons are acceptable.

ETL (Extract, Transform, Load) and Change Data Capture (CDC) are essential in modern data integration and processing methodologies. To further our understanding in this area, let's examine each step of the ETL process in depth to learn how CDC can enhance it. 

Extract: 

In this starting phase, data is collected from different sources. Usually, data is extracted in bulk via batch-based database queries. However, this approach can be inefficient because source tables are constantly updated. The advantage of using CDC is that it addresses this flaw by extracting data in real-time or near-real-time, leading to a more continuous stream of change data. This ensures that the target repository can more precisely mirror the current state of the source application.

Transform: 

This phase transforms extracted data to fit the target repository’s format and structure. In the past, ETL tools were used to convert entire data sets in a staging area before the loading sequence. This process was much more time-intensive with larger datasets. CDC can optimize this process by continuously loading data as it changes at the source and then transforming it within the target system. Examples include cloud-based data warehouses or data lakes, and this process is more capable of keeping up with the increasing size and complexity of modern datasets.

Load: 

In the final phase, the transformed data is loaded into the target repository, which can be analyzed by BI or analytics tools. With normal ETL, this process occurs after transformation. Data tends to be loaded before transformation in CDC and the more modern ELT. With this improved method, it is possible to have faster and more flexible data integration. 

Change Data Capture (CDC): CDC is a method that increases ETL by capturing and delivering the minor changes made to the data in real-time. When using CDC, multiple improvements to an ETL pipeline can be obtained. These improvements include simplifying and speeding up the process and creating more reliable and up-to-date data. CDC can be paired alongside ETL or its more modern alternative, ELT, to guarantee accurate and efficient data integration and processes.

Benefits/Advantages of Change Data Capture

CDC provides multiple advantages that can significantly improve data management and utilization across different use cases. It can benefit an organization by capturing changes from database transaction logs and publishing them to other destinations like cloud data lakes, data warehouses, or message hubs.

Here is a list of some of the benefits that CDC can give to an organization:

• Incremental Data Synchronization: CDC will only synchronize changed data, which is more efficient than replicating an entire database. With this strategy, time can be saved, and that can boost data accuracy, which is vital for master data management (MDM) systems and workloads in production.
• Real-Time Analytics: Real-time analytics allows organizations to find, analyze, and act on real-time data changes. This can enable a personalized and up-to-date customer experience. For example, a restaurant could offer a customized menu based on previous historical data. Similarly, retailers can optimize offers and prices depending on buyer patterns. This all results in faster decision-making.
• Minimal Source-to-Target Impact: CDC can capture incremental updates with minimal impact on production databases, enabling high-volume data transfers to analytics targets without disrupting daily operations.
• Rapid Data Pipeline Deployment: Using CDC can accelerate the development of offline data pipelines, reducing the need for complex scripting. With this setup, data engineers can focus on tasks that drive business value; this lowers the total code of ownership (TCO) as it reduces the dependencies of highly skilled application users.
• Elimination of Bulk Loading: CDC enables real-time data integration, eliminating the need for batch windows and bulk loads. The added gain here is that it ensures continuous ETL processes and much better communication between the data sources and the repositories.
• Incremental Data Transfer: As data is transferred in small increments, CDC is better at minimizing the strain on system resources when compared to bulk data loads. This significantly improves overall system efficiency.
• Seamless Migrations: CDC has the necessary capabilities to support real-time database migrations without downtime. The upside is that it can facilitate real-time analytics, synchronization, and applications across various distributed systems.
• Consistent Data Across Locations: Data consistency is far more accurate due to the CDC's adeptness at ensuring that multiple data systems remain in sync. This is invaluable as it leads to well-maintained, time-sensitive applications and environments 

Common Issues / Disadvantages of Change Data Capture
 

1. The problem with the first four CDC methods above is that they take snapshots at specified points in time. If a data element changes multiple times between snapshots, only the last change is captured. The interim changes are completely missed and lost forever. This has huge implications for use cases that depend on analyzing every change event, such as fraud detection and AI/ML modeling.
2. Trigger-based CDC can impact the performance of the source database because triggers run on the database tables as data changes are made. With every transaction, it takes compute cycles to record the change in a separate table, so the system is slowed by this extra processing. This may also cause a slight delay, which affects latency.
3. Event programming is a complex undertaking. This method involves writing the capture code into applications outside of the database. For example, when data changes, the application executes additional code to capture the change and record it into a separate table. This can get complicated, impact the performance of the application, and require application code changes for every schema change and every new change event in the database.
    

Use Cases & Examples of Change Data Capture

Change Data Capture has been a popular way to replicate and back up mission-critical databases for many years. Today, there are a growing number of use cases that take advantage of the near-real time nature of CDC. CDC is ideal for rapidly changing data, since it extracts and ingests data with extremely low latency. A sampling of popular use cases include:

• Cloud migration: CDC can enable faster and more accurate data migration by continuously capturing changes from on-site databases and replicating them to cloud-based platforms. This will reduce downtime and guarantee data consistency for the transition. For example, an extensive e-commerce database can be moved from an in-house data center to AWS or Azure without disrupting ongoing operations.
• Operational analytics: Allowing faster data ingestion allows the CDC to help businesses perform real-time analytics on their operational data. Take, for example, a retail chain that can use the CDC to update its data warehouse with continuous sales transactions. This leads to an immediate analysis of sales trends and inventory levels that can positively affect businesses making informed choices.
• Fraud detection: The CDC can provide a much better assessment when detecting potential fraud as it enables real-time monitoring of all transactions. For instance, a financial institution can use the CDC to track account activities and flag suspicious transactions. This allows for instant investigation and responses to prevent fraudulent actions.
• Real-time marketing campaigns: Implementing CDC can improve customer engagement as it keeps customer data up to date in real-time. For example, an online retailer can use CDC to capture customer interactions and purchase behaviors, allowing them to send personalized offers and promotions immediately. Marketing campaigns can benefit from these types of insights.
• AI/ML: When using AI/ML, companies gain reduced cycle times and more accurate models because the CDC can ensure that machine learning models are trained on the most current data. In another example, a logistics company can continuously use the CDC to update its predictive models with real-time shipping data. This results in a better and more accurate delivery prediction and optimizes route plans.
• Replication: CDC can support automated and simple or complex data integrations as it continuously syncs data across multiple databases or systems. A multinational company can use the CDC to ensure its global CRM systems are always in sync. This offers a comprehensive perspective of sales activity and customer interactions across several locations.
• Audit: The CDC can enable the recreation of data from any state of a business at any point. This is highly important for audit and compliance purposes. Let’s take a look at another example: a healthcare provider. The healthcare provider can use the CDC to maintain a historical record of patient data changes; this guarantees that they can comply with regulatory requirements and provide an accurate audit trail as needed.