spark-fast-tests
databricks-nutter-projects-demo
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| spark-fast-tests | databricks-nutter-projects-demo | |
|---|---|---|
| 6 | 1 | |
| 418 | 48 | |
| - | - | |
| 0.0 | 6.9 | |
| 3 months ago | about 2 years ago | |
| Scala | Python | |
| MIT License | - |
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spark-fast-tests
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Lakehouse architecture in Azure Synapse without Databricks?
I was a Databricks user for 5 years and spent 95% of my time developing Spark code in IDEs. See the spark-daria and spark-fast-tests projects as Scala examples. I developed internal libraries with all the business logic. The Databricks notebooks would consist of a few lines of code that would invoke a function in the proprietary Spark codebase. The proprietary Spark codebase would depend on the OSS libraries I developed in parallel.
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Well designed scala/spark project
https://github.com/MrPowers/spark-fast-tests https://github.com/97arushisharma/Scala_Practice/tree/master/BigData_Analysis_with_Scala_and_Spark/wikipedia
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Unit & integration testing in Databricks
If the majority of your stuff is not UDF-based there is an OS solution to run assertion tests against full data frames called spark-fast-tests. The idea here is similar in that you have a it notebook that calls your actual notebook against a staged input reads the output and compares it to a prefabed expected output. This does take a bit of setup and trial and error but it’s the closest I’ve been able to get to proper automated regression testing in databricks
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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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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.
databricks-nutter-projects-demo
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Unit & integration testing in Databricks
You can also use the approach described here https://github.com/alexott/databricks-nutter-projects-demo
What are some alternatives?
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
dbx - 🧱 Databricks CLI eXtensions - aka dbx is a CLI tool for development and advanced Databricks workflows management.
chispa - PySpark test helper methods with beautiful error messages
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
spark-daria - Essential Spark extensions and helper methods ✨😲
dagster - An orchestration platform for the development, production, and observation of data assets.
beavis - Pandas helper functions
snipp.in - Fast, Light-weight, Notes, Snippet manager and code editor directly inside your browser
Metabase - The simplest, fastest way to get business intelligence and analytics to everyone in your company :yum: