Data Education Center: What Is Change Data Capture (CDC)?

Change Data Capture (CDC) is a method used to identify and track changes made to data within a database, table, or other data sources. The process captures data modifications—such as additions, updates, and deletions—and records them to ensure data consistency and accuracy across systems. CDC is essential for maintaining real-time data integrity and supporting efficient data integration and migration strategies.

Core Components of CDC:

• Database Log Files: Most CDC operations rely on database log files to monitor and report data changes. These logs track every transaction, ensuring that changes are accurately recorded.

• Triggers: Database triggers can be set to detect changes and log them as they occur. However, this method can be complex and difficult to maintain.

• Timestamps: By comparing timestamps or checking for the most recent changes, CDC can identify modifications made since the last update.

• Snapshot CDC: This method involves comparing entire data sets to identify differences. It is useful when timestamps or unique row identifiers are not available.

Why Change Data Capture Matters

Change Data Capture (CDC) is essential for modern businesses due to its ability to ensure data integrity and real-time synchronization across systems. This capability is increasingly important as organizations shift towards data-driven decision-making and digital transformation.

1. Ensures Real-Time Data Integration

• CDC allows businesses to keep their data up-to-date in real-time across various systems, ensuring that all applications and processes are working with the most current information.

• Real-time data integration is critical for applications such as financial transactions, where timely updates are crucial for accuracy and security.

2. Enhances Data Accuracy and Consistency

• By continuously capturing changes, CDC maintains data consistency across multiple platforms and environments.

• This accuracy is essential for analytics, reporting, and operational decision-making, providing a reliable foundation for data-driven strategies.

3. Improves Operational Efficiency

• CDC reduces the need for full data loads by capturing only the changes, which significantly lowers system load and resource consumption.

• This efficiency translates to cost savings and better performance for critical business applications.

4. Supports Regulatory Compliance

• Many industries require accurate and up-to-date data records for regulatory compliance. CDC ensures that all changes are tracked and recorded, facilitating easier compliance with data privacy laws and regulations.

• Maintaining an audit trail of data changes helps in meeting compliance requirements and conducting audits smoothly.

5. Facilitates Cloud Migrations

• As businesses move to cloud-based systems, CDC plays a vital role in ensuring seamless data migration with minimal downtime.

• It allows continuous data replication from on-premises systems to cloud environments, ensuring data is always synchronized.

6. Enhances Business Intelligence and Analytics

• With CDC, data is available in real-time for analytics, enabling businesses to make quicker and more informed decisions.

• Real-time analytics powered by CDC can significantly improve business operations, customer experiences, and strategic planning.

How Change Data Capture Works

Change Data Capture (CDC) involves detecting and capturing changes in data, then propagating these changes to target systems. This ensures that all systems are synchronized with the latest data updates.

1. Capturing Data Changes

• Insert, Update, and Delete Operations: CDC captures all types of data modifications—insertions, updates, and deletions—in the source database.

• Schema Changes: It can also capture changes in database schema, such as modifications to table structures or data types.

2. Methods of Implementing CDC

• Timestamp-Based CDC: Utilizes timestamp columns to identify changes. It queries the database to retrieve rows modified since the last extraction. This method is simple but may not capture deletions effectively.

• Trigger-Based CDC: Uses database triggers to capture changes. Triggers are set on tables to log changes in shadow tables. While effective, it can impact database performance due to the overhead of maintaining triggers.

• Log-Based CDC: Reads transaction logs where all database changes are recorded. This method is efficient and minimally intrusive, as it does not require additional schema changes or impact database performance significantly.

3. Push vs. Pull CDC

• Push CDC: The source database pushes updates to target systems as changes occur. This method ensures real-time updates but requires reliable connections to prevent data loss.

• Pull CDC: Target systems periodically poll the source database to retrieve changes. This method is easier to implement but may introduce latency as updates are batched between polls.

4. CDC in ETL Pipelines

• Extract: CDC extracts changes in real-time, providing a continuous stream of data updates.

• Transform: Data transformations are applied to the captured changes before loading them into target systems.

• Load: The transformed data is loaded into target repositories, such as data warehouses or lakes, ensuring they are up-to-date.

5. Applications and Use Cases

• Data Replication: Ensures consistent data across databases, which is crucial for disaster recovery and high availability.

• Real-Time Analytics: Supports applications that require immediate data updates, such as fraud detection and dynamic marketing campaigns.

• Microservices Integration: Facilitates the synchronization of data between monolithic and microservices architectures.

Benefits of Using Change Data Capture

Change Data Capture (CDC) offers numerous benefits for businesses seeking to maintain real-time data accuracy and operational efficiency. By capturing changes in data as they occur, CDC enables organizations to stay competitive and responsive in a fast-paced digital environment.

1. Real-time Data Loading and Synchronization

• CDC allows businesses to load data into data warehouses in real-time, ensuring that analytics and reporting are based on the most current data.

• It connects different database systems in near real-time, making it beneficial for organizations with multiple databases handling various data streams.

2. Minimizes Disruptions to Production Workloads

• By efficiently processing data changes, CDC minimizes disruptions to production workloads.

• Continuous updates to data marts with real-time data, such as sales and customer information, help maintain smooth operations.

3. Improves Master Data Management Systems

• CDC enables quick data extraction from multiple sources, continuously updating an organization's master data management system.

• This keeps critical data safe, secure, and up-to-date, which is vital for accurate business insights and operations.

4. Integrates Apps with Incompatible Databases

• CDC allows integration of software tools with in-house databases that are otherwise incompatible, offering flexibility in application selection.

• This ensures businesses can choose the best tools without worrying about database compatibility issues.

5. Accelerates Reporting and Business Intelligence

• Faster data movement between databases enables timely reporting and enhanced business intelligence capabilities.

• This leads to quicker decision-making and better strategic planning.

6. Reduces Pressure on Operational Databases

• By creating a copy of operational databases for secondary access, CDC reduces the load on primary systems.

• This helps in managing high traffic without affecting the performance of critical applications.

Challenges of Implementing CDC 

Implementing real-time Change Data Capture (CDC) can significantly enhance data integrity and operational efficiency, but it comes with a set of challenges. These challenges can impact the effectiveness and reliability of the CDC processes.

Data Integration and Interoperability

Ensuring seamless integration and interoperability across different systems is one of the most critical challenges in implementing CDC solutions.

• System Compatibility: Different databases and applications may use varied formats and protocols, making it challenging to achieve seamless integration. This can lead to data silos and hinder real-time data sharing.

• Interoperability Standards: The lack of standardized protocols for data exchange can create barriers. Systems need to be compatible to ensure smooth data flow and real-time updates.

Performance and Scalability

As data volumes grow, maintaining the performance and scalability of CDC systems becomes increasingly challenging.

• High Volume Data Processing: Processing large volumes of data in real-time requires robust infrastructure and efficient algorithms. High latency can degrade the performance of CDC systems, making them less effective.

• Resource Utilization: Real-time CDC systems can be resource-intensive, requiring significant CPU and memory resources. This can impact the performance of other applications running on the same infrastructure.

Security and Compliance

Ensuring data security and compliance with regulatory requirements is paramount for any CDC implementation.

• Data Privacy: Real-time data capture and sharing can expose sensitive information to unauthorized access if not properly secured. Ensuring data privacy through encryption and access controls is essential.

• Regulatory Compliance: Organizations must comply with various data protection regulations such as GDPR, HIPAA, and CCPA. Real-time CDC systems need to be designed to meet these regulatory requirements.

Complexity and Maintenance

Implementing and maintaining CDC solutions can be complex and require continuous monitoring and updates.

• Implementation Complexity: Setting up CDC systems involves configuring multiple components and ensuring they work together seamlessly. This requires specialized knowledge and expertise.

• Ongoing Maintenance: Continuous monitoring and maintenance are required to keep the CDC systems running smoothly. This includes regular updates, troubleshooting, and performance tuning.

Addressing these challenges requires a comprehensive approach that includes robust infrastructure, standardized protocols, and stringent security measures.

Best Practices for Implementing Real-Time CDC 

To overcome the challenges associated with real-time CDC, organizations should adopt best practices that ensure efficiency, security, and reliability.

Standardize Data Formats and Protocols

Standardizing data formats and communication protocols can enhance interoperability and streamline data integration.

• Adopt Industry Standards: Use industry-standard formats like JSON, XML, or CSV for data exchange. Standardized protocols such as RESTful APIs can facilitate seamless communication between systems.

• Data Normalization: Normalize data to ensure consistency across different sources. This can help in avoiding data discrepancies and improving the accuracy of CDC processes.

Optimize Performance and Scalability

Enhancing the performance and scalability of CDC systems is crucial for handling large volumes of data in real-time.

• Efficient Data Processing: Use efficient algorithms and data structures to process large volumes of data quickly. Implementing parallel processing and distributed computing can enhance performance.

• Scalable Infrastructure: Utilize cloud-based infrastructure that can scale dynamically based on data volume and processing requirements. This can help in managing peak loads and ensuring consistent performance.

Enhance Security and Compliance

Implementing robust security measures and ensuring compliance with regulatory requirements is essential for protecting sensitive data.

• Data Encryption: Encrypt data at rest and in transit to protect it from unauthorized access. Use strong encryption algorithms and key management practices.

• Access Controls: Implement strict access controls to ensure that only authorized personnel can access sensitive data. Regularly audit access logs and review permissions.

• Compliance Audits: Conduct regular compliance audits to ensure that the CDC systems meet all regulatory requirements. Stay updated with changes in data protection regulations and adjust policies accordingly.

Simplify Implementation and Maintenance

Simplifying the implementation and maintenance processes can reduce complexity and improve the reliability of CDC systems.

• Automated Deployment: Use automation tools for deploying and configuring CDC systems. This can reduce manual errors and streamline the setup process.

• Continuous Monitoring: Implement continuous monitoring to detect and resolve issues promptly. Use monitoring tools that provide real-time insights into the performance and health of the CDC systems.

• Regular Updates: Keep the CDC systems updated with the latest patches and enhancements. Regularly review and optimize the system configurations to ensure optimal performance.

By adopting these best practices, organizations can effectively implement real-time CDC and leverage the benefits of accurate and timely data updates.

Real-Time Change Data Capture (CDC) Solutions

IRI offers several valuable Real-Time Change Data Capture (CDC) solutions to address the challenges in data integration, performance, security, and test data maintenance. More specifically, the IRI Ripcurrent CDC module in the IRI Voracity data management platform provides flexible and efficient methods for capturing, replicating, and masking  database data as it changes in real-time, and supporting alerts (notifications) related to schema changes.

Capture and Refresh MS SQL, MySQL, Oracle, and PostgreSQL Targets

IRI Ripcurrent facilitates the real-time capture and refresh of data in various database environments. This tool monitors database logs for any changes to the source data, including new rows, updates, and deletions, ensuring that the target databases are always up-to-date.

• Multi-Database Support: Ripcurrent supports MS SQL, MySQL, Oracle, and PostgreSQL so it can work across different database environments.

• Real-Time Monitoring: It captures changes as they occur, ensuring immediate reflection in the target databases.

• Log-Based CDC: By monitoring database logs, Ripcurrent efficiently detects and processes changes without impacting database performance.

IRI Ripcurrent for DB Data Replication

Ripcurrent can be used to replicate data in source database schemas into target schema (e.g., in lower environments for testing) as changes occur in real-time. That is, when a new row is added (inserted) or changed in the source table, it will be copied into or updated in the target table. Similarly, if a row is deleted from the source, it will be removed from the target. This method of replication provides several benefits:

• Efficiency: By leveraging log-based monitoring, Ripcurrent ensures minimal impact on database performance while replicating data changes immediately.

• Scalability: It can handle large volumes of data changes, making it suitable for big data environments.

• Accuracy: Ensures that all changes are captured accurately and reflected in the target databases in real-time. 

IRI Ripcurrent for Real-Time Data Masking

Similarly, data replicated to the target can be masked at the same time using the same data classes and masking rules established for IRI FieldShield operations which are typically performed in batch jobs. In this way, data in lower environments can be protected as they are kept in sync using pre-defined deterministic, referentially consistent data masking rules.

Batch Change Data Capture (CDC) Solutions

Offline Delta Reporting 

Alternatively, IRI offers the option to report on deltas (changes) offline using the Sort Control Language (SortCL) 4GL program in IRI Voracity or IRI CoSort. This data-centric CDC approach is not dependent on database logs, and provides a flexible and powerful solution for managing and reporting on data changes in a variety of sources.

• Multiple Source Analysis: Supports the analysis of changes from various sources, including multiple RDBs and flat files.

• Segmentation: Enables segmentation of inserts, deletes, and updates, providing detailed insights into data changes.

• Flexible Reporting: Allows the generation of meaningful BI reports against the updated values, facilitating better data analysis and decision-making.

• No Complex Designs: Eliminates the need for log sniffers, DB-specific triggers, or other complex designs, simplifying the implementation process.

Comprehensive CDC Features

This CDC solution comes with a range of features designed to enhance data management and operational efficiency. These features include:

• Data Transformation: The ability to cleanse, calculate, aggregate, and transform data as it is captured.

• Data Protection: Supports field-level encryption and other data masking functions to protect sensitive information.

• Custom Reporting: Generates detail and summary reports in custom layouts, providing actionable insights.

• Data Refresh: Refreshes data warehouse tables with real-time updates, ensuring that BI tools always have access to the latest data.

• Bulk Loading: Supports bulk loading of pre-sorted data through DB load utilities, optimizing data integration processes.

• Archiving and Replication: Outputs data to flat files for archiving, replication, or hand-offs, ensuring the new data is preserved and easily accessible.

Change Data Capture Wizard in IRI Workbench

The Change Data Capture Wizard in the IRI Workbench GUI for Voracity simplifies the setup and management of offline CDC processes. This tool provides an intuitive interface for configuring CDC operations, making it easy to implement and maintain.

• User-Friendly Interface: The wizard offers a user-friendly interface for setting up and managing CDC processes, reducing the complexity of implementation.

• Automated Operations: Automates CDC operations, minimizing the need for manual intervention and ensuring consistent performance.

Big Data Change Capture

For big data change capture scenarios, you can start with a SELECT query in the compatible IRI FACT (Fast Extract) tool for Oracle, DB2, etc. This allows you to offload rows generated after a certain timestamp to a flat file. FACT's capabilities include:

• Unqualified Unloads: Fast extraction of entire transaction sets for subsequent analysis.

• Count New, Modified, or Missing Records: Track changes in your reports or consult SortCL's runtime statistics for inner and outer match counts at each join.

• Labeling and Logging: Analyze transaction data to identify red flags and assess trends.

Conclusion

Change Data Capture (CDC), whether performed in real-time or offline, is used to track and leverage changes in source data. By leveraging IRI's comprehensive suite of tools, including Ripcurrent, Voracity, CoSort, and FACT, organizations can ensure accurate and timely data updates, enhance data security, and maintain regulatory compliance. To learn more about how CDC can benefit your organization, explore these solutions and schedule an online demonstration.