Modern businesses operate in a constantly changing environment, and their data becomes outdated quickly. Relying on old information puts them at a competitive disadvantage. Organizations need instant personalization and smart operations. Traditional batch processing methods cause significant delays. Data can be hours or even days old before it reaches analytical systems.
Change Data Capture (CDC) solves this core problem. It offers a continuous, low-impact stream of information, keeping different systems perfectly synchronized. CDC ensures that analytics, applications, and cloud platforms always use the most current data available.
In this article, we will discuss what change data capture is, how it works, and why it is necessary for modern data systems.
Table of Contents
What is Change Data Capture (CDC)?
Change data capture is a set of software design patterns. It detects and manages incremental changes at the data source. CDC allows users to apply these changes to any other systems or services that rely on the same data. It captures changes as they happen.
CDC is a proven data integration pattern. It tracks when and what changes occur in data. It then alerts other systems and services that must respond to those changes.
Core Purpose
The core purpose of CDC is to maintain consistency and functionality across all systems that rely on data. Data is fundamental to every business. CDC ensures all interested parties of a particular dataset are accurately informed of changes. They can react accordingly. They can refresh their own version of the data or trigger business processes. It eliminates the need for full data loads.
Why Is CDC Crucial for Modern Data Systems?
Modern data systems demand real-time insights and efficient data flow. CDC addresses these needs and offers significant advantages over traditional batch processing.
Real-time Data Synchronization
CDC captures and propagates data changes as they occur, ensuring that all connected systems remain updated in real-time. This is vital for maintaining seamless data sharing. It also ensures consistency across an ecosystem where multiple systems or databases need to stay synchronized without delays.
Enhanced Efficiency and Faster Data Processing
CDC minimizes the need for manual intervention or traditional batch processing, leading to more efficient data processing pipelines and reduced latency. This ensures downstream systems have access to the latest information promptly. CDC is considered an improved version of traditional ETL (Extract, Transform, Load) processes because it captures and moves only the changes, significantly optimizing the overall process.
Scalability and Flexibility
CDC allows event-driven architectures to scale easily to handle increasing data volumes and evolving business needs by decoupling components and leveraging asynchronous communication. It enables systems to scale horizontally while maintaining responsiveness and reliability.
Microservices Architecture Support
CDC facilitates seamless data transfer and synchronization between source datasets and multiple destination systems in a microservices environment. It ensure consistency across distributed architectures.
Reduced Pressure on Operational Databases
CDC optimizes the data identification and transfer process by capturing only specific updates since the last synchronization. it significantly alleviating the load on operational databases compared to transferring entire datasets.
| Benefit Category | Description | Key Advantage |
| Real-time Data Synchronization | Keeps data fresh, accurate, and consistent across multiple systems by continuously monitoring and replicating changes instantly. | Immediate data availability for critical decisions. |
| Powering Event-Driven Architectures | Captures database changes as events, triggering real-time processing and synchronization in downstream systems. | Reduced latency and seamless system updates without full data extractions. |
| Enhanced Efficiency and Faster Data Processing | Synchronizes only changed data, significantly more efficient than full database replication. | Optimized resource usage and quicker data processing. |
| Scalability and Flexibility | Handles high-volume data changes efficiently with minimal impact on source systems. | Supports growing data volumes and adaptable system designs. |
| Microservices Architecture Support | Bridges traditional databases and modern cloud-native microservices, ensuring data consistency across distributed domains. | Seamless data flow and conflict avoidance in complex architectures. |
| Reduced Pressure on Operational Databases | Minimizes CPU load by avoiding frequent polling and continuously feeding data to analytics targets without disruption. | Preserves source system performance and operational stability. |
How Does Change Data Capture Work?
Change Data Capture operates by continuously monitoring a source system for data modifications. It then transmits these changes to a downstream system. The process involves several key steps.
- Change Identification: The CDC system continuously scans the transaction log. It finds any changes, such as inserts, updates, or deletes, identifying what changed and which rows were affected.
- Capture Relevant Information: Once a change occurs, the CDC system captures relevant information from the transaction log. This includes the type of change (insert, update, delete), the timestamp, and the affected rows.
- Data Storage: The captured data is stored in a separate repository, tables, or a dedicated CDC database. This ensures data analysis without affecting the source database performance.
- Delivery: After recording the change, the CDC tool updates the data warehouse or other target systems accordingly. The captured changes are streamed directly to the target (replication) or processed through transformations (streaming ETL).
- Ongoing Monitoring and History: Continuous monitoring and management of the CDC process are essential for handling errors. CDC naturally maintains a history of changes over time, which is valuable for analytics and auditing.
What Are the Different Methods for Implementing Change Data Capture?
Various methods exist for implementing CDC. Each method has distinct mechanisms, advantages, and disadvantages. The choice depends on specific use cases and system requirements.
Log-based Change Data Capture
- Mechanism: This method reads database transaction logs, such as MySQL’s binlog, PostgreSQL’s WAL, or Oracle’s redo log, to capture changes in real-time. It operates at a low level.
- Advantages: It offers low latency and high accuracy, directly monitors changes at the database level, and has minimal impact on the source system’s performance because transactions are unaffected.
- Disadvantages: It requires privileged access to transaction logs. Its effectiveness relies on proper log retention settings. Implementation complexity is high and requires careful management of log access and retention.
Trigger-based Change Data Capture
- Mechanism: This method uses database triggers. Triggers are attached to source table events (inserts, updates, or deletes). They automatically record changes. Triggers record changes in a separate audit or shadow table.
- Advantages: It is straightforward to implement on databases that support triggers. It ensures immediate change capture. It provides fine-grained control over data capture. It captures all change types, including deletes.
- Disadvantages: It can add extra load to the database. Triggers run on every change. It may complicate schema changes if not managed carefully. It creates tight coupling between the application and data capture logic.
Time-based Change Data Capture
- Mechanism: This method compares timestamps in a column to detect changes. It relies on a dedicated column that records the last modified time for each record.
- Advantages: It is a simple approach when systems automatically update timestamps. It can be built with native application logic. It does not require external tooling.
- Disadvantages: Accuracy depends on consistent clock synchronization and reliable timestamp updates. It only captures deletes if soft deletes are used. It can introduce latency. Changes are only detected at fixed intervals. It adds additional overhead to the database.
Query-based Change Data Capture
- Mechanism: This method periodically queries the source database to check for changes. It uses a timestamp or version column. It runs SQL queries on the source database at scheduled intervals.
- Advantages: It is simple to implement when log access or triggers are unavailable. It provides an accurate view of changed data. It uses native SQL scripts.
- Disadvantages: It can introduce latency. Changes are detected at fixed intervals. It can increase load if polling is too frequent. It cannot capture DELETE operations easily. It is highly inefficient for production-grade, real-time pipelines.
Push and Pull Approaches (for Change Delivery)
- Push Method: The source database actively updates the target systems. This ensures near real-time data synchronization. It is analogous to a news broadcaster providing live updates.
- Pull Method: The target system regularly polls the source database. It retrieves any changes. This lightens the load on the source database. It might introduce a slight delay.
- Mitigation: Both methods often use a messaging system. This acts as a bridge. It ensures data waits safely until it can reach its destination.
| Feature | Log-based CDC | Trigger-based CDC | Time-based CDC | Query-based CDC |
| How it Works | Reads database transaction logs to capture changes in real-time. | Uses database triggers to log changes in an audit table. | Compares timestamps in a column to detect changes. | Periodically queries for changes using a version number or other criteria. |
| Latency | Near real-time (low latency). | Immediate (triggers execute instantly). | Depends on polling frequency (low to moderate latency). | Scheduled intervals (can introduce delays). |
| System Overhead | Low (does not require querying tables). | High (triggers run on every change). | Low to Moderate (relies on timestamps). | Moderate (depends on polling frequency). |
| Implementation Complexity | High (requires access to transaction logs and proper retention). | Medium-High (requires creating triggers and maintaining an audit table). | Low (simple if timestamps are automatically managed). | Low (relies on simple SQL queries). |
| Supports Deletes? | Yes (captured from logs). | Yes (if logged in the audit table). | Only if soft deletes (deleted_at) are used. | Needs extra tracking (e.g., a separate delete table). |
| Best Use Case | High-volume, real-time replication where minimal database load is crucial. | Small to medium workloads that need instant change capture. | When timestamps are automatically updated and frequent polling is feasible. | When log-based CDC and triggers are unavailable, but periodic updates are acceptable. |
What Challenges Can Arise with CDC, and How Are They Overcome?
Implementing CDC pipelines can present several challenges. Addressing these challenges requires careful planning and robust solutions.
Bulk Data Management
- Challenge: Handling the bulk of data requiring extensive changes can diminish CDC efficiency. This is notable during initial data loads or large-scale updates. CDC pipelines efficiently handle real-time changes. Accommodating sudden and significant modifications requires extra attention.
- Solution: Implement efficient tools like distributed processing frameworks and optimize deployment strategies by scaling resources dynamically based on usage patterns. Enhancing the CDC pipeline with advanced data processing techniques can also help.
Schema Changes
- Challenge: Schema changes can significantly impact the CDC replication process. This can lead to data corruption, inconsistencies, and system failures if not properly managed. When altering table structures, the CDC pipeline must adapt.
- Solution: Advanced CDC solutions often employ metadata and intelligent algorithms to adjust to schema changes. Using schema registry tools can track and manage schema versions, ensuring graceful handling of backward-compatible schema changes.
Data Integrity
- Challenge: Maintaining data consistency and integrity is crucial. This ensures replicated data is accurate and reliable. It protects the integrity of the target system.
- Solution: Implement strong validation checks, robust error handling, and reconciliation mechanisms. Versioning and rollback mechanisms can provide traceability and quick correction of transformed data.
Resource Consumption
- Challenge: CDC can become resource-intensive depending on the volume of data changes. Frequent changes exert pressure on system resources. CDC introduces an agent process on the server. This complicates scaling the application database.
- Solution: Implement optimization strategies such as throttling mechanisms to control data processing rates. Fine-tuning parameters like batch size and parallelism can align with the system’s capacity.
| Challenge | Description of Challenge | Key Solutions |
| Bulk Data Management | CDC efficiency diminishes with large initial data loads or extensive bulk updates, potentially overwhelming pipelines. | Prepare Kafka clusters for increased load; use distributed processing frameworks; employ dynamic resource scaling; take initial snapshots for historical data. |
| Schema Changes | Altering table structures can lead to data corruption, inconsistencies, or system failures in the CDC pipeline if not managed properly. | Automate schema change detection and adaptation; invest in CDC solutions designed for schema changes; use schema registries. |
| Data Integrity | Ensuring replicated data remains accurate and consistent is crucial to protect the target system’s reliability. | Establish robust data governance frameworks; implement data lineage tracking; conduct regular data quality checks; foster data accountability. |
| Resource Consumption | Continuous tracking and high volumes of data changes can make CDC resource-intensive, impacting system performance. | Push data to message queues (e.g., Kafka) for scalability; prioritize log-based CDC; consider direct event emission from applications; utilize real-time data platforms. |
What Are the Best Practices for Scaling the CDC?
Scaling CDC pipelines is essential for handling growing data volumes and maintaining performance. Effective scaling involves optimizing various components of the data pipeline.
- Optimize Log-Based CDC: Ensure transaction logs are configured to retain necessary change data for CDC processes to capture it. Utilize tools like Apache Kafka with Debezium, which are designed for high-throughput change streams.
- Partitioning: Implement data partitioning to distribute the workload across multiple nodes or instances. For instance, partition Kafka topics based on logical keys (e.g., user ID) for even distribution and parallel processing of change events.
- Batch Processing: Consider batching changes where real-time processing isn’t critical to reduce overhead associated with processing individual changes. CDC tools can be configured to group changes into batches for periodic processing.
- Load Balancing: Distribute the CDC workload across multiple consumers or processors using load balancers or distributed stream processing frameworks to prevent bottlenecks.
- Horizontal Scaling: Design the CDC solution to scale horizontally by adding more instances or nodes. Ensure the architecture supports distributed processing and load balancing.
Real-World Applications of Change Data Capture
Leading technology companies and various industries leverage CDC for critical operations. These applications highlight CDC’s versatility and impact.
1. Netflix
Utilizes Apache Kafka and Apache Flink for its CDC pipeline to achieve real-time data synchronization and analytics.
- Details: Netflix uses a Data Mesh platform. It processes trillions of events daily. This platform leverages Apache Flink and Kafka. CDC connectors extract data from data stores. They put it into Kafka topics. Netflix uses Flink’s SQL engine as a changelog processor. It processes CDC data. Flink analyzes streaming transaction data. It detects anomalies. This supports personalized content recommendations based on live user interactions. It also supports fraud detection.
2. Uber
Employs Apache Kafka and their open-source project, Cadence, for CDC to achieve real-time data synchronization across multiple microservices and data stores.
- Details: Uber’s MySQL fleet uses Storagetapper for CDC. Storagetapper captures changes (inserts, updates, deletes) from MySQL binlog. It streams them to Apache Kafka. Data is further ingested into an Apache Hive data store. The system handles upstream schema changes, transformations, and format conversions. Cadence is a distributed orchestration engine. It executes asynchronous long-running business logic.
3. Airbnb
Uses Debezium (an open-source CDC tool) in combination with Apache Kafka to capture changes from their MySQL databases.
- Details: Debezium monitors and records row-level changes in real-time. It transfers them to Apache Kafka. Kafka serves as the foundation for real-time data ingestion and streaming. Airbnb uses Kafka Connect to ingest data from various sources into Kafka topics. Kafka Streams performs stream processing tasks. These include filtering, aggregating, and enriching data in real-time.
Other Industry Use Cases
- Finance: Continuously tracking changes in financial records detects fraudulent activity. It enables real-time transaction monitoring.
- Retail: CDC tracks customer activity on mobile phones. It dynamically adjusts pricing or offers. Inventory and supply chain management. It helps avoid stock-outs. It supports lucrative pricing decisions.
- IoT (Internet of Things): CDC efficiently integrates massive amounts of real-time data generated by IoT devices. This enables predictive maintenance and real-time monitoring. It supports real-time monitoring of IoT sensors in smart factories.
- Manufacturing: Monitors production processes, ensures smooth information flow between production systems and inventory management.
- Logistics & Supply Chain: Tracks inventory changes in real-time. It optimizes routes. It reduces transportation costs. It tracks changes in the supply chain, including orders and shipping updates.
- Telecommunications: Supports real-time billing, network optimization, and customer experience management by capturing and processing data changes from network elements, billing systems, and customer interaction channels.
Conclusion
Incorporating Change Data Capture (CDC) into system design is essential for ensuring real-time data synchronization and supporting event-driven architectures. CDC’s ability to track database changes and promptly update connected systems is vital for maintaining data consistency and enabling responsive operations.
It plays a critical role in a wide array of applications, from real-time analytics to efficient data integration. By minimizing data latency and optimizing data flow, CDC allows organizations to derive timely insights and maintain data freshness across their entire ecosystem.
Adhering to best practices, such as optimizing log-based tracking, managing schema changes, and ensuring fault tolerance, enables organizations to effectively handle large data volumes.It also helps maintain reliable, consistent data flows.
Reference
- Change Data Capture (CDC): What it is and How it Works – Striim, https://www.striim.com/blog/change-data-capture-cdc-what-it-is-and-how-it-works/
- Modern data management with real-time Change Data Capture, https://www.tinybird.co/blog-posts/real-time-change-data-capture
- What is Change Data Capture? | Informatica, https://www.informatica.com/resources/articles/what-is-change-data-capture.html
- What is change data capture (CDC)? – Red Hat, https://www.redhat.com/en/topics/integration/what-is-change-data-capture
- What Is Change Data Capture (CDC)? How It Works and Why It Matters – Domo, https://www.domo.com/glossary/change-data-capture
- How To Use CDC To Optimize Your ETL Process + Examples – Estuary, https://estuary.dev/blog/cdc-etl/
- Change Data Capture (CDC) in Event-Driven Microservices | Orkes Platform, https://orkes.io/blog/change-data-capture-cdc-in-event-driven-microservices/
- The Ultimate Guide to Event-Driven Architecture Patterns – Solace, https://solace.com/event-driven-architecture-patterns/
- What is Real-Time Data Replication? – SnapLogic, https://www.snaplogic.com/glossary/real-time-data-replication
