quinn
etl-markup-toolkit
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quinn | etl-markup-toolkit | |
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9 | 7 | |
567 | 5 | |
- | - | |
9.2 | 0.0 | |
21 days ago | about 3 years ago | |
Python | Python | |
- | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
quinn
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PySpark OSS Contribution Opportunity
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 .
There are a lot of issues in the quinn repo with a "good first issue" tag if you'd like to get started: https://github.com/MrPowers/quinn/issues
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Invitation to collaborate on open source PySpark projects
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.
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Pyspark now provides a native Pandas API
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.
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Is Spark - The Defenitive Guide outdated?
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.
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Ask HN: What are some tools / libraries you built yourself?
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
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Open source contributions for a Data Engineer?
I've built popular PySpark (quinn, chispa) and Scala Spark (spark-daria, spark-fast-tests) libraries.
etl-markup-toolkit
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How to keep track of the different Transformations done in an ETL pipeline?
Not sure if it meets your exact requirements, but I maintain an open source project that enables spark transformations as configuration, and part of that capability is reporting, including logging of columns in vs columns out, row counts, etc... It's very early stage but perhaps could be useful - https://github.com/leozqin/etl-markup-toolkit
What are some alternatives?
chispa - PySpark test helper methods with beautiful error messages
spark-daria - Essential Spark extensions and helper methods ✨😲
spark-rapids - Spark RAPIDS plugin - accelerate Apache Spark with GPUs
mara-pipelines - A lightweight opinionated ETL framework, halfway between plain scripts and Apache Airflow
PySpark-Boilerplate - A boilerplate for writing PySpark Jobs
null - Nullable Go types that can be marshalled/unmarshalled to/from JSON.
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
lowdefy - The config web stack for business apps - build internal tools, client portals, web apps, admin panels, dashboards, web sites, and CRUD apps with YAML or JSON.
flintrock - A command-line tool for launching Apache Spark clusters.
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
sparkmagic - Jupyter magics and kernels for working with remote Spark clusters
dagster - An orchestration platform for the development, production, and observation of data assets.