debezium VS Airflow

They manage data in the application layer and your original data stays where it is. This way data consistency is no longer an issue as it was with streaming databases. You can use Change Data Capture (CDC) services like Debezium by directly connecting to your primary database, doing computational work, and saving the result back or sending real-time data to output streams.

Both of these articles mention a key player, Debezium. In fact, Debezium has had a place in the modern infrastructure. Let's use a diagram to understand why.

This might be overkill, but you could use an extra tool like https://debezium.io to capture logs about all creates, updates, and deletes in your table

If you're considering a relational database for an event-driven architecture, check out Debezium. It lets you stream changes to relational databases, and subscribe to change events.

Debezium

https://debezium.io/ comes to mind as an open source product, but there are a gazillion of these tools out there.

Contrary to what it sounds, it’s quite straightforward. The database changes are captured via Debezium and published as events on Apache Kafka. ClickHouse consumes those changes in partial order by Kafka Engine. Real-time and eventually consistent.

Contributing to Apache Airflow's open-source project immersed me in collaborative coding. Experienced maintainers rigorously reviewed my contributions, providing constructive feedback. This ongoing dialogue refined the codebase and honed my understanding of best practices.

In week Two, I contributed to the Apache Airflow repository.

Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows.

Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring.

AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic!

I agree there are many options in this space. Two others to consider:

- https://airflow.apache.org/

- https://github.com/spotify/luigi

There are also many Kubernetes based options out there. For the specific use case you specified, you might even consider a plain old Makefile and incrond if you expect these all to run on a single host and be triggered by a new file showing up in a directory…

You can try and contribute some new connectors/operators for workflow managers like Airflow or Airbyte