scala VS Airflow

Recently I had to revisit the "JVM languages universe" again. Yes, language(s), plural! Java isn't the only language that uses the JVM. I previously used Scala, which is a JVM language, to use Apache Spark for Data Engineering workloads, but this is for another post 😉.

8. Scala - $96,381

Scala

Big Scala vibes here, see also [1].

1: https://github.com/scala/scala/blob/v2.13.11/src/library/sca...

For details, refer to the release notes on GitHub: * https://github.com/scala/scala/releases/tag/v2.13.11 * https://github.com/scala/scala/releases/tag/v2.12.18

Scala is the next-generation Java virtual machine (JVM) language that is rapidly gaining popularity as a modern alternative to Java.

Compiling Scala without a SAT solver is probably too difficult.

The CNF Converter is a gem.

https://github.com/scala/scala/blob/v2.13.5/src/compiler/sca...

In addition to Structured Query Language(SQL), we can also use a variety of different programming languages, such as Python, Java, JavaScript, R, Julia, Scala, or any other programming language as long as it supports a basic database connection and functions to perform all of those operations, to connect to databases and perform more advanced query operations on the data. This gives us greater flexibility and allows us to apply custom-created logic to the data.

Based on Lambda Calculus, developed by Alonzo Church in the 1930s, functional programming has been implemented in mainstream programming languages like JavaScript, Python and Java. Programming languages like Erlang, Haskell or Scala are better suited for functional programming as they are designed to be functional first.

Scala

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