In today’s fast-paced data environment, Change Data Capture (CDC) transforms how organizations handle and synchronize their expanding data volumes. According to the Market Analysis Report, the global data management market size was valued at USD 89.34 billion in 2022 and is expected to grow at a compound annual growth rate (CAGR) of 12.1% from 2023 to 2030, the growing emphasis on real-time data access and actionable insights is driving this trend. This blog provides an in-depth look at Debezium CDC, including how to it and Hevo to manage and synchronize data changes effectively.
What is CDC, and Why is it Important?
Change Data Capture (CDC) allows you to recognize data that has changed in the source system and capture the changes in the destination systems. Most companies using databases use CDC.
Let us take a look at an example, as shown in the figure below.
Some customer data is stored in a MySQL database. The data which is stored in the MySQL database is used as a transactional database, but you also want to store the same data in a data warehouse, which will be used for analytics purposes.
There is a relational database where all the sales and customer data is stored. Whenever something changes on the customer’s end, you need the data to be updated. Suppose you have a stream processing job that does some kind of analytics or processing and shows the insights on a dashboard. In that case, you want to ensure that whenever your customer data changes, the stream processor processes the new information in real-time.
In this scenario, the source system is the customer data in MySQL, and the target system is the data warehouse, the sales data, and the stream processor.
CDC plays an important role in enhancing the system architecture.
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Overview of Debezium CDC as a Tool
Debezium CDC is an open-source platform that implements log-based CDC, captures the changes made to transactional databases, and streams them as change events. Debezium works well with MySQL, Postgres, MongoDB, or any other database you use. The transaction log records all changes made to the data, including inserts, updates, and deletes.
Once the changes are captured, Debezium serializes them as change events. These change events have a well-defined schema that describes the changes applied to the source database. The change events are then published by Debezium into a messaging system like Apache Kafka, enabling multiple downstream systems to consume them in real-time.
The deployment options include deploying Debezium as a source connector on Kafka Connect.
Key Features and Capabilities of Debezium CDC
The following are the features and capabilities of Debezium:
- Debezium has a log-based CDC that captures the complete, ordered history of data changes, avoiding missed changes during downtimes or between polling intervals.
- It captures near real-time data change with low event delays while avoiding the increased CPU load associated with frequent polling, which can otherwise strain the database and cause delays or missed updates.
- Polling requires a column to indicate the last updated timestamp to track changes, which can be difficult to maintain across all tables. However, log-based Debezium does not rely on such indicators and does not impact the data model.
- Debezium CDC offers optional snapshots, configurable filters for schemas, tables, and columns, data masking for sensitive information, monitoring via JMX, and ready-to-use message transformations for routing, filtering, and event flattening.
- Debezium CDC is log-based, which helps capture all the deletes to ensure that there are identical data sets between the source and replication targets.
Debezium Architecture
The figure below shows that the Debezium architecture consists of several key components. Primarily, source connectors monitor database changes and publish them as events to Kafka Topics. These events are then streamed by sink connectors to downstream systems. One of the components in the figure is Kafka Connect, which is a framework for integrating Apache Kafka with external data sources through connectors.
When a new record is added to the database, the source connector detects and records the change, and pushes it to a Kafka Topic. The sink connector then streams this record to a system like Elasticsearch, where it can be consumed by an application or service. This entire process occurs in real-time, ensuring minimal delay and performance impact.
Challenges and Limitations of Using Debezium CDC
Even though Debezium is one of the first open-source CDC frameworks to gain popularity, it has some complications that require considerable time and effort to overcome. Some of the limitations of using Debezium are as follows:
- Tables may temporarily freeze due to snapshotting, which might impact database operations.
- Large Data Manipulation Language (DML) events can cause backlogs, which might lead to delays in processing all the changes.
- Continuous DevOps is necessary, requiring constant management of CPU resources to prevent data throttling and avoid potential data loss in Kafka Topics with short retention periods.
- Complex migrations and schema changes are manual.
Introduction to Hevo: Simplifying CDC
Debezium is effective for log-based CDC, but it has some drawbacks. Using Debezium CDC can require significant manual effort and resource management. Some tools, such as Hevo, are specifically designed to overcome the limitations of traditional CDC solutions.
Overview of Hevo’s Capabilities
Hevo is a no-code data pipeline platform that addresses many of the limitations associated with tools like Debezium. It offers a seamless solution by automating complex CDC processes and simplifying data integration tasks. Hevo includes built-in pre-load and post-load transformation features, which help reduce the manual effort required by data and engineering teams. The pre-load transformation automatically formats and cleans data on the fly, while the post-load transformation runs models to prepare data for analytics.
How Hevo Automates the Complexities of CDC
Hevo’s CDC feature automatically updates any changes from data sources to the destination. This helps streamline the entire data integration process. Following are some benefits of using Hevo for CDC:
- Capture and replicate real-time data changes without any manual intervention.
- Maintain consistent and up-to-date data with it’s reliable CDC mechanisms.
- Achieve faster data reporting because it is always synchronized and ready for analysis.
Comparison of Debezium vs. Hevo
| Feature | Debezium | Hevo Data |
|---|
| Type | Open-source CDC (Change Data Capture) tool | Cloud-based ETL/ELT data integration platform |
| Primary Functionality | Streams database changes as events | Batch and near real-time data integration and streaming capabilities |
| Architecture | Built on Apache Kafka, uses Kafka Connect for integration | Cloud-native architecture with auto-scaling capabilities |
| Data Capture Method | Captures row-level changes from databases using transaction logs | Supports ETL and ELT processes with Change Data Capture (CDC) |
| Connectors | Supports various databases through Kafka Connect connectors | Offers 150+ source and destination connectors |
| Data Transformation | Limited transformation capabilities; focuses on data streaming | Supports data transformation via Python scripts or drag-and-drop UI |
| Scalability | Horizontally scalable through Kafka’s architecture | Auto-scaling for optimal performance with growing data volumes |
| Use Cases | Best for real-time data streaming and event-driven architectures | Suitable for organizations needing lightweight ETL/ELT workloads |
| Deployment | Self-hosted or managed on cloud services | Fully managed cloud service |
| Security Features | Robust data security, including encryption, role-based access control | Robust data security including encryption, role-based access control |
| Pricing | Open-source, free to use (with potential costs for Kafka infrastructure) | Subscription-based pricing model |
Conclusion
What features will you prioritize to ensure real-time data accuracy in a CDC solution? Efficient Change Data Capture is essential for keeping your data accurate, timely, and consistent across systems. While tools like Debezium offer powerful CDC capabilities, they often come with complexities that require significant manual effort.
Modern platforms like Hevo address these challenges by automating the CDC process, making data integration seamless, and reducing the need for constant oversight. With features such as no-code setup, automated schema management, and real-time data synchronization, Hevo enables data teams to focus on higher-level strategies, confident that their data is always current and reliable. Leveraging Hevo can help businesses streamline their data workflows and overcome the limitations of traditional CDC solutions.
References
FAQ on Debezium CDC
What is CDC Debezium?
Debezium CDC is an open-source tool that captures real-time changes in a database and streams them as events to systems like Kafka, ensuring data is consistently up-to-date across multiple platforms.
What is the difference between Debezium CDC and Kafka?
Debezium captures and streams real-time database changes, while Kafka is a distributed messaging platform that transports and processes these changes as events. Debezium mainly acts as a connector that feeds data into Kafka, which then handles the data distribution to various systems.
How does Debezium CDC work?
Debezium monitors database logs to capture real-time changes like inserts, updates, and deletes. It then streams these changes as events to platforms like Kafka, allowing other systems to process and react to the data in near real time.
Radhika has over three years of experience in data engineering, machine learning, and data visualization. She is an expert at creating and implementing data processing pipelines and predictive analysis. Her knowledge of Big Data technologies, Python, SQL, and PySpark helps her address difficult data challenges and achieve excellent results. With a Master's degree in Data Science from Lancaster University, she uses her analytical skills to develop insightful and engaging technical content for the data business.
