Evaluating Change Data Capture Tools: A Comprehensive Guide
Evaluating Change Data Capture Tools: A Comprehensive Guide
Data Engineering Tools Evaluation Series
TL;DR Aswin and I are thrilled to announce the release of the first version of our comprehensive guide for evaluating Change Data Capture.
CDC Evaluation Guide Google Sheet Link: https://bit.ly/cdc-evaluation-guide
CDC Evaluation Guide Github Link: https://github.com/EvalBench/cdc
Change Data Capture (CDC) is a powerful technology in data engineering that allows for continuously capturing changes (inserts, updates, and deletes) made to source systems. CDC tools fuel analytical apps and mission-critical data feeds in banking and regulated industries, with use cases ranging from data synchronization, managing risk, and preventing fraud to driving personalization.
Why CDC is More Relevant in Unified Data Architecture
As we advance into the Gen AI era, Change Data Capture (CDC) systems are emerging as crucial components of the ever-evolving data architecture. The ability to capture and propagate data changes in real time opens up numerous opportunities and addresses several contemporary business and technical challenges. Here are some key reasons why CDC is becoming more relevant than ever.
The Dominance of the Lakehouses and the Mutation Support
Lakehouses have become a standard pattern in data infrastructure, combining the best features of data lakes and warehouses. Unlike data lakes, which are predominantly append-only, lakehouses support data mutation natively. The support for the mutation feature means they can handle updates and deletions seamlessly, an ability that enhances real-time synchronization through CDC (Change Data Capture) tools. As a result, lakehouses support more dynamic and flexible data architectures, catering to a broader range of analytics and operational workloads. For instance, in a fast-paced retail environment, lakehouses can ensure that inventory data remains up-to-date and accurate in the data warehouse, optimizing supply chain efficiency.
Modernizing Legacy Systems
Many businesses still rely on legacy systems like Mainframes and SAP for their core operations. However, these businesses must unlock the value within these systems and leverage it in modern, cloud-based analytical environments. CDC tools facilitate nonintrusive data extraction and real-time integration from these legacy systems into modern platforms without disrupting ongoing transactions. This capability is crucial as it allows businesses to modernize their data infrastructure incrementally, ensuring a smooth transition and continuous value extraction from legacy investments.
Real-Time Business Demands
In today’s fast-paced business environment, responding to real-time events is a competitive advantage. Whether it’s launching targeted marketing campaigns, optimizing supply chains, or personalizing customer experiences, businesses are increasingly demanding real-time insights and actions. CDC enables real-time data flows from operational systems to analytics platforms, ensuring decision-makers can access the most current data. This immediacy is critical for applications that require up-to-the-minute accuracy and responsiveness.
Emergence of Warehouse-Native Product Analytics
Another significant trend is the shift towards warehouse-native product analytics. Instead of relying on separate analytical databases, many organizations embed analytics directly within their data warehouses. This approach simplifies data architecture and enhances performance by reducing data movement and latency. CDC is integral to this model, ensuring that any changes in transactional data are instantly available for analysis within the data warehouse. This seamless integration supports more sophisticated and timely product analytics, driving better business outcomes.
Microservices and the Outbox Pattern
The adoption of microservices architecture has transformed how applications are developed and deployed. However, managing data consistency across microservices can be challenging. A common solution is the outbox pattern, where changes are written to an outbox table and then asynchronously processed. CDC systems can efficiently capture and propagate these outbox changes to other services or analytics platforms. This approach ensures data consistency and supports the decoupling of microservices, enabling more scalable and resilient architectures.
AI/LLM Advancement and Context-Rich Applications
Advancements in Artificial Intelligence (AI) and large language models (LLMs) are driving the need for context-rich applications like Retrieval-Augmented Generation (RAG) and Knowledge Graphs. These applications require comprehensive and up-to-date contextual information to function effectively. CDC provides real-time data feeds to populate and update these knowledge bases continuously. By capturing and integrating data changes from diverse sources, CDC helps build and maintain the context-rich environments that modern AI applications demand.
Types of CDC Techniques and Their Challenges
Timestamp-Based CDC
Timestamp-based CDC relies on a timestamp column that tracks the last modification time of a record. It identifies changed records by comparing timestamps, making it less invasive and easy to implement for batch feeds.
Challenges:
Modify the database schema to include timestamp columns. Most packaged software backends lack this, requiring alternative extraction methods.
Missed changes occur if the timestamp is not updated correctly.
Unsuitable for databases without native modification timestamp support.
Impacts source system performance during query execution.
Does not handle hard deletes.
The Schema evolution management pushes to the pipeline code to manage.
Table-Diff CDC
Table-diff CDC compares the current state of the data with a previous snapshot to identify changes. This method is often a last resort when other CDC techniques are infeasible.
Challenges:
Highly resource-intensive and slow for large datasets.
Requires significant storage for maintaining snapshots.
Complex to implement and manage.
Trigger-Based CDC
Trigger-based CDC uses database triggers to capture changes. Triggers automatically execute specified actions when events like inserts, updates, or deletes occur. This method is straightforward to implement and compatible with many databases.
Challenges:
It can introduce significant overhead on the database.
Complex to manage and debug, especially in high-transaction environments.
Potential performance degradation if not carefully optimized.
Log-based CDC
Log-based CDC reads the database transaction log to capture changes. It is highly efficient with minimal impact on the source database, and can handle high volumes of changes with low latency. This technique focuses on most real-time use cases and the evaluation document. However, access to the transaction log is required, which may not always be feasible depending on the database system.
Challenges:
Requires deep integration with the database system.
May face issues with proprietary log formats.
Log retention policies need careful handling to avoid data loss.
How to Evaluate a CDC tool
Evaluating a Change Data Capture (CDC) tool requires a comprehensive understanding of various critical categories. These categories ensure the CDC tool is efficient, reliable, and suitable for the organization's needs. Below are the essential evaluation criteria:
1. Bootstrap Capabilities
Bootstrap capabilities are crucial for initializing the CDC process by capturing the initial data state before incremental changes. An effective CDC tool should:
- Support full initial data loads seamlessly.
- Provide mechanisms for handling large datasets efficiently.
- Ensure minimal impact on source systems during the bootstrap phase.
2. Source Connector Ecosystem
The source connector ecosystem determines the tool's ability to integrate with various data sources. Key considerations include:
- Wide range of supported databases and applications.
- Compatibility with both on-premise and cloud-based sources.
- Regular updates and support for new data sources.
3. Target Connector Ecosystem
This category evaluates the CDC tool's ability to deliver data to various targets. An optimal CDC tool should:
- Support various target systems, including databases, data warehouses, and data lakes.
- Ensure compatibility with cloud and on-premise environments.
- Facilitate seamless data delivery to real-time analytics platforms and other endpoints.
4. Essential Features
The core functionalities that make the CDC tool robust and effective include:
- Low-latency data capture and delivery.
- Reliable change data capture mechanisms (e.g., log-based, trigger-based).
- Support for schema evolution and data type transformations.
5. Data Management and Transformation
Effective data management and transformation capabilities are essential for maintaining quality and usability. The CDC tool should:
- Provide data filtering, enrichment, and transformation features.
- Ensure data consistency and integrity during capture and delivery.
- Support complex transformation logic and integration with external ETL processes.
6. Integration and Extendability
A CDC tool's ability to integrate with existing systems and extend its functionalities is crucial. Considerations include:
- APIs and SDKs for custom integrations.
- Compatibility with data integration and orchestration tools.
- Support for extensibility through plugins and custom scripts.
7. Deployment
Deployment flexibility ensures the CDC tool fits within the organization's infrastructure. Key aspects include:
- Support for various deployment models (e.g., on-premise, cloud, hybrid).
- Ease of installation and configuration.
- Scalability to handle growing data volumes and increased change frequencies.
8. Vendor Support and Community
Strong vendor support and an active community are vital for ongoing success. Important factors are:
- Professional support and training services are available.
- Comprehensive documentation and knowledge base.
- Active user community for peer support and knowledge sharing.
9. Security and Monitoring
Ensuring data security and robust monitoring capabilities are non-negotiable. The CDC tool should:
- Implement strong authentication and authorization mechanisms.
- Provide encryption for data in transit and at rest.
- Offer comprehensive monitoring and alerting features for operational visibility.
10. User Experience
A user-friendly interface and ease of use significantly enhance productivity. Key elements include:
- Intuitive user interface and user experience design.
- Clear and comprehensive reporting and dashboards.
- Streamlined workflows for common tasks and troubleshooting.
Evaluating CDC tools against these categories ensures a holistic assessment, helping organizations select tools that align with their technical requirements and strategic objectives. This structured approach facilitates informed decision-making, enabling seamless data integration and real-time analytics capabilities.
What is Next?
In this series of data tool evaluations, we aim to bring sharable intelligence to our community. Historically, benchmark studies have dominated software evaluation, often showing vendor bias or lacking practical value. We aim to establish standard evaluation criteria to help our community build educated data infrastructure tailored to their business needs. By combining our collective learning, we can develop efficient data engineering practices.
We invite your support and contribution to this evaluation series. To facilitate open collaboration, we have released the evaluation criteria under the MIT license on GitHub. You can review, contribute to, and refine the criteria to ensure they meet the highest standards. Together, we can create a robust and adaptable framework for evaluating data tools that benefit everyone.
I’m a data engineer; how can I contribute?
Please contribute additional evaluation criteria that we should include in the evaluation matrix.
Share your experience of evaluating the CDC tool. It can be a blog link or a short note in README.
If you want to share your evaluation experience over a podcast, please fill out this form.
I’m a CDC software maker; how can I contribute?
Please contribute additional evaluation criteria that we should include in the evaluation matrix.
Share your customer case study using your CDC tool. It can be a blog link or a short note in README.
If you want to share your evaluation experience over a podcast, please comment below.
References
https://shopify.engineering/capturing-every-change-shopify-sharded-monolith
https://medium.com/brexeng/change-data-capture-at-brex-c71263616dd7
https://www.wix.engineering/post/change-data-capture-at-deviantart
https://bytes.swiggy.com/architecture-of-cdc-system-a975a081691f
https://www.confluent.io/blog/how-bolt-adopted-cdc-with-confluent-for-real-time-data-and-analytics/
https://www.coinbase.com/blog/soon-for-near-real-time-data-at-coinbase-part-1
https://medium.com/@wyaddow/planning-your-tests-for-change-data-capture-cdc-e80c462330e1