quinn VS dagster

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.

My recommendation is to continue on with the tutorial, then look at one of the larger example projects especially the ones named “project_”, and you should understand most of it. Of what you don't understand and you're curious about, look into the relevant concept page for the functions in the docs.

I found this example that helped me - https://github.com/dagster-io/dagster/tree/master/examples/project_fully_featured/project_fully_featured

I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home

In Helm terms: there are 2 charts, namely the system: dagster/dagster (values.yaml), and the user code: dagster/dagster-user-deployments (values.yaml). Note that you have to set dagster-user-deployments.enabled: true in the dagster/dagster values-yaml to enable this.

Dagster seemed really cool when I looked into it as an alternative to airflow. I especially like the software defined assets and built-in lineage which I haven't seen in any other tool. However it seems it does not support RBAC which is a pretty big issue if you want a self-service type of architecture, see https://github.com/dagster-io/dagster/issues/2219. It does seem like it's available in their hosted version, but I wanted to run it myself on k8s.

Dagster? https://dagster.io

One that I haven't seen on here yet: dagster