Building and maintaining data pipelines can be an uphill task for individuals relying on limited strategies and tools. Individuals have to choose between reliable sources of data and available data storage areas, making it tedious to make changes in between. Change Data Capture (CDC) is an approach that involves making data changes-insert, delete and update real-time data being delivered to a data warehouse, data lake or an analytical store. Not only does it maximise efficiency, but it also improves and facilitates real-time data replication. The approach is reliant on using database triggers, tracking changes via transaction logs (Khalid Abdelaty, 2025) and making changes for downstream applications. Data engineers processing changed data feeds (slowly changing dimensions - SCD processes ) are split between 2 decisions:
Type 1- Retain the latest data by overwriting existing data. Anytime a change is made, old data is replaced by new data, translating to no records of the old data are retained. The approach is used in fields where the old data is no longer needed, such as updating customer information in retail stores.
Type 2 - Keep a history of changes to the data. New versions of the changed data are created alongside old data (What Is Change Data Capture (CDC)? | Databricks on AWS, 2025). The data is also timestamped to help a user track the log history on changes made in a record. In real-life applications, the approach is used in areas such as studying customer segmentation data on fields such as their addresses when carrying out analysis.
To better understand CDC, the following concepts have been expounded:
Change Detection
A CDC system pinpoints changes in the source system as they occur. This simply means logs on updates, inserts and removal of records are stored and may be used for analytical purposes at a future date.
Real-time
CDC allows the data pipelines to host real-time information, allowing the system to rely on current and recent data, should an individual embark on making changes.
Delta-driven data
Describes tracking changes made to data using rows. Mainly employed by Delta Databricks tools, delta tables rely on Change Data Feed (CDF) to track and process changes. It acts as a stream of modifications, enabling data modifications while supporting event-driven data pipelines that accommodate real-time data analytics by avoiding the need to reprocess entire datasets (What Is Change Data Capture (CDC)? | Databricks on AWS, 2025). Databricks recommends Delta Live Tables (DLT) tools, which simplify the implementation of CDC pipelines handling out-of-order records of faulty changes, providing data quality and activity monitoring. CDF activities are guided by:
Authorising the Change Data Feed (CDF)
When a CDF is enabled on a delta table, it automatically triggers the Delta Lake runtime, which records change events- update, insert for every data wire.
CDF generation
The CDF is a forward-looking log capturing all row-level changes made in the Delta table.
Processing Changes.
As a byproduct of Delta Live Tables, the feed is consumed downstream by events relying on tools such as Databricks Lakeflow Declarative Pipelines to process changes in real-time or near real-time.
Applying changes to target tables
To allow consistent data updates and changes, the changes made are applied to all data tables.
Metadata
Each captured change(insert, update and delete) includes metadata and a sequential number to capture the order in which the change was made.
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Idepotent processing
Ensures a CDC system doesn’t alter the final state of a system. Consumers basically track a change, usually assigned a unique identifier, transaction-message IDs from the CDC stream, and, if an event has been processed, by querying a database. If the ID exists, the order has been processed and the message is disregarded; if not, the order is processed and a log is generated to avoid reprocessing. Idepotent CDC processing empowers a consumer system to handle potential duplicates in the change stream. It ensures that applying the same change event multiple times doesn’t cause irreversible changes or errors to the system. Idempotent processing is important to CDC as it ensures data reliability- system issues cause CDC changes to be re-delivered, consistency- verifies that only one change or application can be successfully carried out, eliminating duplicates and resilience - should the system experience a challenge, it reboots and re-processes the events without the negative effects.
Implementing idempotency CDC processing
- Assigning unique IDs Messages or change events are assigned unique, non-sequential ID values by the producing service. It is indicated as a Kafka header and included in the message payload.
- Checking for Duplicates Before processing the data or changes, the producer service checks for the existence of the message ID in the dataset or dedicated database table.
- Process or Discard Should the message ID be found, the producer system marks it as a duplicate and proceeds to discard it, and an update is made to ensure the process is not repeated. If the message is not found, the consumer system starts a database transaction, processes the message, executes the required business logic, and commits the transaction while storing the message ID to prevent any future duplication.
- Record Processing Upon the completion of the above task, the message ID is inserted into the database, marking it as processed.
The four commonly recognised CDC methods are:
Log-based CDC
Reads database translation logs- Write Ahead logs (WAL) to identify changes instantly as they occur. Its competitive advantage lies in its ability to operate at a low level, capturing changes with minimum disruption. PostgreSQL is an example of a tool which relies on log-based CDC.An example of a log-based
-- Enable logical replication
ALTER SYSTEM SET wal_level = logical;
-- Create a logical replication slot to capture changes
SELECT pg_create_logical_replication_slot('cdc_slot', 'pgoutput');
-- Fetch recent changes from the WAL
SELECT * FROM pg_logical_slot_get_changes('cdc_slot', NULL, NULL);
replication.
`
Trigger-based CDC
Uses triggers attached to source table events (updates, inserts and deletes) to record changes. An upside with the approach is that, if not carefully managed, it may add extra loads to the database schemas should one fail in managing it. It is more suitable for moderate transaction volumes.
Polling-based CDC
The system checks for changes using a timestamp or version column. However, the technique exposes the system to latency challenges as changes are only detected at fixed intervals. The approach is highly recommended in places where real-time access to logs is unavailable, though a slight delay in detecting changes may be experienced.
Time-based CDC
Reliant on a column which records the last time a change was made. A comparison made by the system checks the logs for changes made across the system. It is similar to polling-based CDC with slightly distinct properties, as it requires a more robust mechanism to track changes. It is dependent on consistent modification of the timestamps.
CDC tools
Popular tools that facilitate the implementation of CDC are dependent on the use case.
Debezium
It is an open-source system that captures and streams database changes into systems such as Apache Kafka.It is helpful when streaming data from multiple sources of data, dependent on real-time updates. Use Case: Event-driven architecture and Real-time data streaming.
AWS Data Migration Service (DMS)
Relies on the CDC to continuously replicate data on the AWS system with minimal downtime. It is an excellent choice if one wants to move data to the cloud with ease. Use Case: Cloud migrations and AWS-based architectures.
Apache -Kafka
When paired with tools such as Debezium, it serves as a spine to processing CDC events. It enables a system to synchronise data across multiple consumers, host reliable data pipelines and real-time data analytics. Us Case: Streaming CDC data to a real-time data-driven architecture.
Talent and Informatica
CDC platforms are built to automate ETL pipelines, eliminating manual configurations. Advantageous in complex transaction scenarios where integrated solutions can simplify operations. Use Case: Enterprise-grade ETL solutions with built-in CDC.
Database CDC Native Solutions
Some relational database tools offer native CDC features, reducing the need for external tools.
I. PostgreSQL logical replication
Ii. SQL Server CDC
Iii. My SQL binary log (binlog) replication.
They are used to minimise dependencies on external CDC tools
Real-World Applications.
Cloud Migration
With the world rapidly evolving, building pipelines to an ever-growing database presents experts with storage challenges. Cloud services offered by service providers such as AWS and Microsoft Azure ensure companies pay for cloud storage in response to their usage. CDC helps cloud migration by providing a system architecture that synchronises with real-time data streaming and cloud databases with minimal downtime.
Data Integration
Due to its ability to be used among a wide range of external data services, CDC is an important tool for companies seeking to move data.
Data replication and synchronisation
In data activities that involve multiple consumers and multiple data sources, CDC ensures data is synchronised, facilitating across-the-board changes in real-time. In fields such as inventory management, where customer information is constantly changing, CDC ensures changes do not affect the integrity of the system. Data replication is upheld across the target database, enabling users to monitor changes in real-time.
**References**
Khalid Abdelaty. (2025, February 25). What is Change Data Capture (CDC)? A Beginner’s Guide. Datacamp.com; DataCamp. https://www.datacamp.com/blog/change-data-capture
What is change data capture (CDC)? | Databricks on AWS. (2025, August 4). Databricks.com. https://docs.databricks.com/aws/en/dlt/what-is-change-data-capture
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