Learn Debezium - History, Background & Why CDC

#Introduction

Episode 0 made sure your environment is ready. Now it's time to understand why Debezium exists. Episode 1 answers three big questions: how the capture of changing data has evolved over time, where Debezium comes from, and why change data capture has become the new standard for data engineering.

Many people start using Debezium just by following tutorials, yet understanding its background is far more valuable. By knowing the problem it solves, you'll find it easier to decide when to use CDC, when not to, and how to position it within a data architecture. Let's start from the beginning of the story.

#The Evolution of Data Processing: from Batch ETL to CDC

#The Batch ETL Era

For years, data flow between systems was driven by batch ETL: data was extracted from sources periodically — for example, every night — then transformed and loaded into the target. This pattern is simple and predictable, but it has major weaknesses: latency can reach hours, data is never truly current, and the process is wasteful because it reprocesses the entire dataset even though only a small portion actually changed.

The most common example is a cron job that runs an aggregate query against a transaction table and writes the results to a data warehouse. As long as analytics requirements don't demand real-time data, this pattern is comfortable enough. But the moment an organization wants to make decisions based on fresh data, the limits of the wall quickly become visible.

#The Polling-Based Ingestion Era

To reduce latency, many teams switched to polling: the application periodically asks the database whether there are new rows since the last query, using an updated_at column or an incrementing id. This approach is more responsive, but fragile: the columns don't always exist, deletes are not detected, the continuous query load presses down on the production database, and polling logic piles up in every application.

The query pattern usually looks like this:

Polling query pattern

SELECT id, name, email
FROM customers
WHERE updated_at > ?
ORDER BY updated_at ASC
LIMIT 1000

Notice that the pattern above depends on the updated_at column, which must be maintained diligently by the application. If any write path forgets to update that column, the change slips off the polling radar. Row deletion is also completely undetected unless you use soft deletes, so deleted data is still considered present in downstream systems.

#The Birth of Change Data Capture

Change data capture (CDC) offers a more elegant answer: instead of guessing changes with queries, CDC reads the change records the database already writes to its transaction log. Changes are captured the moment they happen, in their original order, without adding query load to the source database. Debezium is one of the most popular CDC implementations for this approach.

batch ETL (latency in hours)
  └── polling (latency in seconds, extra queries)
        └── CDC (latency in milliseconds, reads the transaction log)

#The History and Journey of Debezium

#The Beginning at Red Hat

Debezium was first developed around 2016 by Red Hat engineers, inspired by early CDC tools such as Maxwell and Red Hat's internal tool for capturing data changes. The name Debezium comes from a German term meaning base or foundation — fitting for a tool that bridges databases and other systems.

#From Open Source to an Apache Project

Debezium's journey moved quickly:

• 2016: Debezium was released as an open source project by Red Hat.
• 2023: it entered the Apache Incubator, marking the seriousness of its community governance.
• 2025: it officially became a top-level project at the Apache Software Foundation.
• 2025-2026: the 3.x releases with full support for the latest Kafka and KRaft mode.

Since becoming an Apache project, the Debezium ecosystem has grown rapidly: dozens of connectors, clean documentation, and an active community that keeps improving release quality. The 3.x version you used in episode 0 is the result of that long journey.

#Debezium's Role in the Event Streaming Ecosystem

Debezium turns your database into an event source. The moment a change happens in the database, Debezium converts it into a structured event and produces it to Kafka. From there, the entire ecosystem can consume it:

• Other applications read events for real-time data synchronization.
• Stream processors such as ksqlDB and Kafka Streams process them directly.
• Sink connectors stream them to Elasticsearch, a data warehouse, or a data lake.
• Data scientists consume events for analytics and machine learning.

This position makes Debezium the foundation between transactional databases and the world of event streaming — a role that matches exactly the meaning of its name.

#CDC versus Polling versus Batch ETL

To make architecture decisions easier, compare these three approaches:

From this table it's clear: CDC wins on almost every dimension when the primary need is data that's always current. However, batch ETL remains relevant for heavy transformations and historical needs, so all three often coexist within a single architecture.

#Use Cases That Motivated Debezium

Debezium was born to solve real-world problems like these:

• Database replication: copying data from the primary database to a read replica or another database in real time.
• Analytics pipelines: streaming data changes to a data warehouse without waiting for the nightly batch.
• Auditing: storing a complete trail of every change — who, when, and how the data changed.
• Materialized views: building data views that update automatically from the stream of changes.
• Microservices data sync: keeping data across microservices in sync through events rather than cross-service queries.

#Conclusion

Episode 1 gave you the context: data processing evolved from slow batch ETL, through fragile polling, toward CDC, which reads the transaction log in real time. Debezium was born at Red Hat around 2016 and is now a healthy Apache top-level project with 3.x releases.

The key takeaways:

• CDC reads the database transaction log, so latency is in milliseconds and the load on the source is low.
• Debezium was born at Red Hat around 2016 and became an Apache project in 2025.
• CDC excels at delete detection, event ordering, and transaction integrity.
• Debezium turns the database into an event source for the entire streaming ecosystem.
• Main use cases: replication, analytics, auditing, materialized views, and microservices sync.

In the next episode 2 we'll cover Debezium core concepts and architecture — how connectors read database change logs, the role of Kafka Connect and Kafka topics, offset, heartbeat, and snapshot mechanisms, as well as their relationship to the schema registry and payload formats. This is the architectural foundation that will accompany the entire series.