How ETL Is Supported By Change Data Capture

We will examine Change Data Capture (CDC), including its definition, operation, advantages of streaming change events, commercial benefits, and use cases. Other change data techniques, including database triggers, will not be covered in this blog; instead, we will only look at data capture in the setting of a streaming ETL via a transaction log.

Change Data Capture: What Is It?

To employ these change events in the destination system—such as a data warehouse, data lake, data app, machine learning models, indexes, or caches—change data capture refers to the act of recording changes made to data in a source system, such as a database. It is also feasible to read once and write several times, which is preferable to a straightforward 1-to-1 replication.

ETL streaming: What is it?

A sort of data integration process known as ETL (extract, transform, load) entails continually taking data from several sources, altering it to meet the requirements of the destination system, and feeding it into the destination system almost instantly.

How does Change Data Capture work?

The following image depicts the database DML (Inserts, Updates, and Deletes) that runs on the database and then speaks to the transaction log. Following that, real-time log-based CDC provides to the destination in the form of an Orders Table.

Databases such as PostgreSQL, MongoDB, and MySQL can track changes such as inserts, updates, and deletes. A common term for Change Data Capture is log-based replication. This alteration is frequently documented in transactional databases by writing to the database transaction log. Also known as a write-ahead log (WAL), which resides in a database directory.

What advantages does Change Data Capture provide businesses?

• Competitive advantage: Organizations and their teams can create real-time customer experiences. And improve business outcomes more quickly when they have access to real-time data.
• Data protection: You may utilize Change Data Capture to audit trails, stop fraud, and restore your systems to a precise moment in time.
• Lower costs: Compared to the query in batches technique, Change Data Capture is more efficient and lightens the burden on your source. You may save system resources and bandwidth when combined with the ability to read from the log. And write information out to several destinations, including data lakes, warehouses, and data applications.

What drawbacks does Change Data Capture have?

While there are no drawbacks to adopting a log-based CDC, there are two issues that might make it difficult to get started:

• Source Database: For log-based CDC, just a few database parameter changes will be necessary. Typically, you should use your production master/primary database instead of a slave database since the transaction log may be accessed from the master/primary.
• Destination: The amount of log-based CDC occurrences can be substantial. It is up to you to determine how many historical change events you want to preserve. And how you want to preserve them. Several ways could be in this case, including upsets, destination table retention policies, and simple scripts to eliminate superfluous past data.

What applications does change data collection have?

1. Streamlined ETL: Change Data Capture removes the requirement for bulk loading and updating at inconvenient batch intervals. And the hassle of worrying about when to run your scheduled processes because it only captures changes.
2. Costs/Impact: Replicating logs effectively can collect additional data while minimizing the impact on the source. Furthermore, we can read once and publish to several destinations using Streamkap. Whether they are the same contents, a subset, or in-stream transformations.

• Audit Trail: Change data capture enables you to replay events from a certain point in time. And save the updated data in a different location. It also lets you record all events as an audit trail.
• Recovery: If a mistake occurs at a certain point in time, it is easier to restore the source to its original form if a thorough record of all system alterations is intact.

How can I begin putting Change Data Capture into practice?

1. Make the source’s Data Capture enabled.
2. Obtain login credentials for the location to which you want to transmit this information.
3. Construct a streaming ETL pipeline with Change Data event handling capabilities. If you choose the open-source path, you should begin with Kafka & Debezium. It will take some time to get things rolling if you are unfamiliar with these technologies. Aside from this, handling schema updates (also known as schema evolution) could need development work.
4. As an alternative, you may utilize Streamkap, which handles schema evolution. And a variety of sources/destinations out of the box, to quickly create your streaming ETL Change Data Capture pipelines.

Conclusion

Companies may expedite their ETL operations by using Change Data Capture (CDC), an efficient way to record and monitor changes to data in real-time. Several techniques, including query-based, trigger-based, and log-based techniques, can be used to accomplish CDC. The most effective approach is log-based, which records any modifications to the data instantly. Organizations may update the destination system with the most recent data from the source system by integrating log-based CDC into Streaming ETL operations. This leads to real-time analytics and well-informed decision-making.

FAQ’s

Is streaming change data capture enabled?

Change data capture serves to collect and send real-time events such as deposits and withdrawals.

What distinguishes CDC from ETL?

The CDC timeline continually loads data as it changes at the source.

What makes streaming data different from ETL?

Streaming ETL makes it easier to make changes to data as it moves.

In ETL, what is change data capture?

A method called “change data capture” (CDC) ensures that changes to a database become available to a new location, such as a data warehouse.