DataGristle
spark-fast-tests
| DataGristle | spark-fast-tests | |
|---|---|---|
| 5 | 6 | |
| 137 | 418 | |
| - | - | |
| 0.0 | 0.0 | |
| 3 months ago | 7 days ago | |
| Python | Scala | |
| GNU General Public License v3.0 or later | MIT License |
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DataGristle
- What are your weekend side projects?
- Instant data model from 1000s of unique files?
- Using Hashing to detect data changes in ELT
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How do you sort a CSV file with several million rows?
DataGristle: this one contains some more unusual csv utilities, and what's in master includes the ability to sort by field names rather than offsets: https://github.com/kenfar/DataGristle
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Open source contributions for a Data Engineer?
DataGristle by u/kenfar who influenced many of us in this sub.
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.
What are some alternatives?
Skytrax-Data-Warehouse - A full data warehouse infrastructure with ETL pipelines running inside docker on Apache Airflow for data orchestration, AWS Redshift for cloud data warehouse and Metabase to serve the needs of data visualizations such as analytical dashboards.
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
chispa - PySpark test helper methods with beautiful error messages
sqlfluff - A modular SQL linter and auto-formatter with support for multiple dialects and templated code.
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.
didact-engine - The REST API and execution engine for the Didact Platform.
spark-rapids - Spark RAPIDS plugin - accelerate Apache Spark with GPUs
spark-daria - Essential Spark extensions and helper methods ✨😲