spark-snowflake
chispa
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| spark-snowflake | chispa | |
|---|---|---|
| 1 | 12 | |
| 196 | 508 | |
| -0.5% | - | |
| 5.3 | 6.5 | |
| 5 days ago | 10 days ago | |
| Scala | Python | |
| Apache License 2.0 | 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.
spark-snowflake
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Why Databricks Is Winning
Snowflake and Databricks are different, sometimes complementary technologies. You can store data in Snowflake & query it with Databricks for example: https://github.com/snowflakedb/spark-snowflake
Snowflake predicate pushdown filtering seems quite promising: https://www.snowflake.com/blog/snowflake-spark-part-2-pushin...
Think both these companies can win.
chispa
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Testing spark applications
Unit and e2e tests using a combination of pytest and chispa (https://github.com/MrPowers/chispa). Custom library to create random test data that fits schema with optional hardcoded overrides for relevant fields to test business logic.
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Spark open source community is awesome
here's a little README fix a user pushed to chispa
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Invitation to collaborate on open source PySpark projects
chispa is a library of PySpark testing functions.
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installing pyspark on my m1 mac, getting an env error
The other approach I've used is Poetry, see the chispa project as an example. Poetry is especially nice for projects that you'd like to publish to PyPi because those commands are built-in.
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Spark: local dev environment
- All Spark transformations are tested with pytest + chispa (https://github.com/MrPowers/chispa)
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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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Show dataengineering: beavis, a library for unit testing Pandas/Dask code
I am the author of spark-fast-tests and chispa, libraries for unit testing Scala Spark / PySpark code.
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Tips for building popular open source data engineering projects
Blogging has been the main way I've been able to attract users. Someone searches "testing PySpark", they see this blog, and then they're motivated to try chispa.
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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?
databricks-nutter-repos-demo - Demo of using the Nutter for testing of Databricks notebooks in the CI/CD pipeline
spark-fast-tests - Apache Spark testing helpers (dependency free & works with Scalatest, uTest, and MUnit)
flintrock - A command-line tool for launching Apache Spark clusters.
spark-daria - Essential Spark extensions and helper methods ✨😲
Apache Spark - Apache Spark - A unified analytics engine for large-scale data processing
quinn - pyspark methods to enhance developer productivity 📣 👯 🎉
dask-gateway - A multi-tenant server for securely deploying and managing Dask clusters.
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.
delta - An open-source storage framework that enables building a Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, and Hive and APIs
null - Nullable Go types that can be marshalled/unmarshalled to/from JSON.
dagster - An orchestration platform for the development, production, and observation of data assets.
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.