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Quinn Alternatives
Similar projects and alternatives to quinn
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Sonar
Write Clean Python Code. Always.. Sonar helps you commit clean code every time. With over 225 unique rules to find Python bugs, code smells & vulnerabilities, Sonar finds the issues while you focus on the work.
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lowdefy
An open-source, self-hosted, low-code framework to build internal tools, web apps, admin panels, BI dashboards, workflows, and CRUD apps with YAML or JSON.
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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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dagster
An orchestration platform for the development, production, and observation of data assets.
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InfluxDB
Build time-series-based applications quickly and at scale.. InfluxDB is the Time Series Platform where developers build real-time applications for analytics, IoT and cloud-native services. Easy to start, it is available in the cloud or on-premises.
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spark-fast-tests
Apache Spark testing helpers (dependency free & works with Scalatest, uTest, and MUnit)
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snipp.in
Fast, Light-weight, Notes, Snippet manager and code editor directly inside your browser
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etl-markup-toolkit
ETL Markup Toolkit is a spark-native tool for expressing ETL transformations as configuration
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Metabase
The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum:
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gutenberg
A fast static site generator in a single binary with everything built-in. https://www.getzola.org
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Nullboard
Nullboard is a minimalist kanban board, focused on compactness and readability.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
quinn reviews and mentions
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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.
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A note from our sponsor - InfluxDB
www.influxdata.com | 30 Jan 2023