Understanding Debezium: Real-Time Data Streaming for Change Data Capture

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Understanding Debezium: Real-Time Data Streaming for Change Data Capture

Debezium is an open-source, distributed platform that continuously captures and streams real-time changes made to databases. In other words, Debezium is a low-latency data streaming platform designed primarily for Change Data Capture (CDC). Through CDC, Debezium captures row-level changes from databases and streams them as events. Applications then consume these events in the same order in which they occur, ensuring they remain up-to-date with the latest data changes.

What’s CDC? 

CDC stands for Change Data Capture, which is used to track and capture changes made to data in a database. It allows systems to capture changes (inserts, updates, and deletes) at the data level and stream those changes to other systems or processes in real-time. CDC is especially useful for ensuring that data across different systems or applications remains synchronized, without needing to perform costly full data refreshes. Instead, CDC enables capturing only the changes that have occurred since the last data capture, reducing overhead and ensuring that systems can stay up to date with the most recent data. 

Since Debezium is built on top of the Kafka environment, it captures and stores every real-time message stream in Kafka topics present inside Kafka servers. In addition, Debezium consists of various database connectors that allow us to connect and capture real-time updates from external database applications like MySQL, Oracle, and PostgreSQL. For example, Debezium’s MySQL connector fetches real-time updates from the MySQL database, while Debezium’s PostgreSQL connector will capture data change from the PostgreSQL database. Applications can then read from these Kafka topics to receive the change events. 

Even if an application crashes or loses its connection, it won’t miss any events. When the application reconnects or restarts, it can resume consuming change events from the last processed record, ensuring data consistency and completeness. 

 

Debezium Architecture 

Most commonly, we deploy Debezium by means of Apache Kafka Connect. Kafka Connect is a framework and runtime for implementing and operating: 

  • Source connectors such as Debezium that send records into Kafka
  • Sink connectors that propagate records from Kafka topics to other systems 

Kafka Connect runs as a separate service from Kafka and is used to move data between Apache Kafka and other systems, like databases, data warehouses, or analytics platforms. 

In a typical Debezium setup: 
  1. Change Data Capture (CDC): Debezium captures changes from a database (e.g., inserts, updates, deletes). 
  2. Kafka Topics: By default, these changes are written to a Kafka topic with a name that matches the database table name. 
Customizing Kafka Topics:

We can customize how Debezium writes change events to Kafka topics,

  • Route records to a different topic: Configuring Debezium to send the change data to a topic with a different name. 
  • Stream data for multiple tables into one topic: Combining the change events from multiple tables into a single Kafka topic. 
Using Kafka Connect to Stream Data to Other Systems:

Once the change events are in Kafka, Kafka Connect can use various sink connectors to move the data to other systems, such as: 

  • Elasticsearch for search and analytics 
  • Data warehouses for storage and analysis 

 

Key Features of Debezium 

  • CDC: The primary use case of Debezium is to implement CDC (Change Data Capture), which allows us to capture and stream real-time data modifications made on external databases.  
  • Data monitoring: Debezium is capable of continuously monitoring, capturing, and streaming row-level modifications made on external database systems such as MySQL, PostgreSQL, and SQL Server.  
  • Data consistency: Since Debezium collects and saves data in log-based CDC format, every real-time data modification or update made on the database is reliably kept and structured in a precise sequence inside the commit log. 
  • Fault-tolerant: Since Debezium is a distributed platform, the application’s architecture is designed to be fault-tolerant and flexible even when any faults or failures occur during the continuous data transfer.  

Conclusion  

Debezium is a powerful tool for change data capture, offering real-time data streaming capabilities and supporting a wide range of databases. Its integration with Kafka makes it scalable and fault-tolerant, making it suitable for various use cases, from data replication and real-time analytics to event-driven architectures and disaster recovery. By capturing and streaming database changes, Debezium helps organizations maintain data consistency, enable real-time insights, and build responsive, event-driven systems. 

 

 

 

 

 

 


Nayan Sagar N K

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