Airflow VS CouchDB

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

CouchDB is a json based database for simple projects. The fork pouchdb offers lots of support for offline.

Apache CouchDB belongs to the family of NoSQL databases. It is a document store with a strong focus on replication and reliability. One of the most significant differences between CouchDB and a relational database (besides the absence of tables and schemas) is how you query data. Relational databases allow their users to execute arbitrary and dynamic queries via SQL. Each SQL query may look completely different than the previous one. These dynamic aspects are significant for use cases where you work exploratively with your dataset but don't matter as much in a web context. Additionally, defining an index for a specific table is optional. Most developers will define indices to boost performance, but the database does not require it.

I just turns out you can't always do that in a real codebase. For example see here:

  https://github.com/apache/couchdb/blob/23efd8e5b1aa96ef01640fec03a5fedc945ba8b9/src/couch_mrview/src/couch_mrview_http.erl#L228

Example: Apache Cassandra, CouchDB.

For non-SQL-based databases, consider MongoDB, or CouchDB, which are very easy to get started with.

The Apache CouchDB server. It stores Annotation data.

CouchDB

You can implement the sync algorithm from scratch, or you can use tools like CouchDB and turtleDB to help you.