Rudderstack VS Airflow

If CDP(such as RudderStack) were a restaurant, then Data Integration is the guy that gets all raw ingrediants from different shops and makes it available to Chef that sorts and combines raw ingrediants to make a dish. The chef can't cook anything without raw ingrediamt. Similarly Data Integration is also an important component in CDP that collects customer data from various sources and them other components unify it and activate it.

More details on GitHub repository - https://github.com/rudderlabs/rudder-server

Are you RudderStack?

In bigger setups, all you want is a data collector and router so that you can feed the data into multiple destinations, depending on the use case. Analytics is just one. Example: https://www.rudderstack.com/ & https://www.rudderstack.com/replace-google-analytics-4-guide...

Check out RudderStack, a Go project to build data pipeline. Our slack is quite active. The best way to contribute is by creating a new integration with your favorite tool. You do not need to rely to too much on existing knowledge about inner workings of the project to do so, so it is beginner friendly.

Interesting. About "Redshift need google sheet sync to table", wouldn't this be more aligned with the responsibility of a CDP(such as RudderStack) as opposed to something we expext a warehouse to do?

The challenge is over. Winners have been announced. When we are ready for the next one, will announce on RudderStack GitHub repo

Super. This is amazing. Sharing your project with the community. If you get a chance, try out RudderStack to build your pipeline.

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