fugue
quinn
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fugue
- FLaNK Stack Weekly 22 January 2024
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Daft: A High-Performance Distributed Dataframe Library for Multimodal Data
Please integrate it with Fugue.
https://github.com/fugue-project/fugue
- Fugue: A unified interface for distributed computing
- [Discussion] Open Source beats Google's AutoML for Time series
- Ask HN: How do you test SQL?
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Replacing Pandas with Polars. A Practical Guide
Fugue is an interesting library in this space , though I haven’t tried it
https://github.com/fugue-project/fugue
A unified interface for distributed computing. Fugue executes SQL, Python, and Pandas code on Spark, Dask and Ray without any rewrites.
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The hand-picked selection of the best Python libraries and tools of 2022
fugue — distributed computing done easy
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[P] Open data transformations in Python, no SQL required
This looks similar to fugue, am I right? How do they compare?
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What the Duck?!
I am looking forward to how Substrait could help removing this friction. It aims to provide a standardised intermediate query language (lower level than SQL) to connect frontend user interfaces like SQL or data frame libraries with backend analytical computing engines. It is linked to the Arrow ecosystem. Something like Ibis or Fugue could become the front and DuckDB the backend engine.
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Pyspark now provides a native Pandas API
There's dask-sql, but I think it is being abandoned for fugue-project. I'm actually excited for this project as it is trying to provide a backend agnostic solution, which would seem like a difficult, lofty goal. I wish them luck.
quinn
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Brainstorming functions to make PySpark easier
We're brainstorming functions to make PySpark easier, see this issue: https://github.com/MrPowers/quinn/issues/83
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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 .
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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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Register Native Functions in PySpark
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
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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.
What are some alternatives?
modin - Modin: Scale your Pandas workflows by changing a single line of code
chispa - PySpark test helper methods with beautiful error messages
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
spark-daria - Essential Spark extensions and helper methods ✨😲
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
spark-rapids - Spark RAPIDS plugin - accelerate Apache Spark with GPUs
mlToolKits - learningOrchestra is a distributed Machine Learning integration tool that facilitates and streamlines iterative processes in a Data Science project.
null - Nullable Go types that can be marshalled/unmarshalled to/from JSON.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
etl-markup-toolkit - ETL Markup Toolkit is a spark-native tool for expressing ETL transformations as configuration
xarray - N-D labeled arrays and datasets in Python
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.