mack VS quinn

There still library form databricks? But I have never used it: https://github.com/MrPowers/mack

I was a Databricks user for 5 years and spent almost all my time inside the IntelliJ IDE developing code. I wrote almost all code in a text editor, unit tested all code (actually authored the popular Scala Spark / PySpark testing libraries: https://github.com/MrPowers/) and had everything up with CI/CD. Lots of OSS PySpark/Scala Spark work too. I only used Databricks notebooks for data exploration and for lightweight notebooks that would invoke functions (that were defined in Python Wheel / JAR files). I am on the Delta Lake team at Databricks now and still do all my work in text editors (see this project: https://github.com/MrPowers/mack) and create lots of examples in Jupyter Notebooks. So I definitely think it's possible to limit notebook exposure.

Great, would love your help. You can also check out the mack project if you'd like to work on a Delta Lake + PySpark project: https://github.com/MrPowers/mack/issues

a couple devs just added a `find_compositite_keys_candidates` function so users can easily identify columns that could be used as a unique identifier in their Delta table.

Fair points. Here's the code repo: https://github.com/MrPowers/mack

We're brainstorming functions to make PySpark easier, see this issue: https://github.com/MrPowers/quinn/issues/83

Adding some README documentation to the README should be quite straightforward. Here's a function that needs to be documented: https://github.com/MrPowers/quinn/issues/52 .

quinn is a library with PySpark helper functions. I need to work through all the open issues / PRs and bump all versions. I should do another release. This library gets around 600,000 monthly downloads.

Pandas syntax is far inferior to regular PySpark in my opinion. Goes to show how much data analysts value a syntax that they're already familiar with. Pandas syntax makes it harder to reason about queries, abstract DataFrame transformations, etc. I've authored some popular PySpark libraries like quinn and chispa and am not excited to add Pandas syntax support, haha.

Here's how I added a create_df method to the SparkSession class: https://github.com/MrPowers/quinn/blob/main/quinn/extensions/spark_session_ext.py

They spent a lot of effort improving the catalyst engine under the hood too and making it easier to extend and improve it in the future. Making it easy to add your own native code to Spark itself. Shameless plug of a blog post I wrote on this subject which basically reiterates what Matthew Powers, author of Spark Daria and quinn, wrote here.

I built daria (https://github.com/MrPowers/spark-daria) to make it easier to write Spark and spark-fast-tests (https://github.com/MrPowers/spark-fast-tests) to provide a good testing workflow.

quinn (https://github.com/MrPowers/quinn) and chispa (https://github.com/MrPowers/chispa) are the PySpark equivalents.

Built bebe (https://github.com/MrPowers/bebe) to expose the Spark Catalyst expressions that aren't exposed to the Scala / Python APIs.

Also build spark-sbt.g8 to create a Spark project with a single command: https://github.com/MrPowers/spark-sbt.g8

I've built popular PySpark (quinn, chispa) and Scala Spark (spark-daria, spark-fast-tests) libraries.