etl-markup-toolkit
PySpark-Boilerplate
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| etl-markup-toolkit | PySpark-Boilerplate | |
|---|---|---|
| 7 | 1 | |
| 5 | 390 | |
| - | - | |
| 0.0 | 2.5 | |
| about 3 years ago | 3 months ago | |
| Python | Python | |
| MIT License | - |
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etl-markup-toolkit
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How do you serialize and save "transformations" in your pipeline?
I have a side project (https://github.com/leozqin/etl-markup-toolkit, if you're interested) that takes transformations as yaml files and outputs step-level logs about each step of the transformation. I've always felt that both artifacts could made searchable using an ELK stack or something... Do you have similar artifacts? Or perhaps there's a way to turn SQL into a structured or semi-structured form to aid in searchability
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Alternative tools to DBT / SQL and Python for writing business logic? Trying to prevent creating a mountain of undocumented spaghetti
My current side project (https://github.com/leozqin/etl-markup-toolkit) is a low code way to express transformations as configuration and run it on pyspark. It also supports abstraction so you can call business logic like a function and has step-level reporting you can load into a metadata table. Usual disclaimers about OSS apply, although I'm happy to answer questions and take contributions.
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How to keep track of the different Transformations done in an ETL pipeline?
Not sure if it meets your exact requirements, but I maintain an open source project that enables spark transformations as configuration, and part of that capability is reporting, including logging of columns in vs columns out, row counts, etc... It's very early stage but perhaps could be useful - https://github.com/leozqin/etl-markup-toolkit
- ETL Markup Toolkit - a spark native tool for describing etl transformations as configuration
- ETL Markup Toolkit - a Spark-native tool for expressing ETL transformations as configuration
PySpark-Boilerplate
What are some alternatives?
mara-pipelines - A lightweight opinionated ETL framework, halfway between plain scripts and Apache Airflow
ibis - the portable Python dataframe library
quinn - pyspark methods to enhance developer productivity 📣 👯 🎉
Optimus - :truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
sparkmagic - Jupyter magics and kernels for working with remote Spark clusters
cookiecutter-django - Cookiecutter Django is a framework for jumpstarting production-ready Django projects quickly.
tdigest - t-Digest data structure in Python. Useful for percentiles and quantiles, including distributed enviroments like PySpark
soda-spark - Soda Spark is a PySpark library that helps you with testing your data in Spark Dataframes
Traffic-Data-Analysis-with-Apache-Spark-Based-on-Mobile-Robot-Data - Mobile robot data were analyzed with Apache-Spark to extract five different statistical result such as travel time, waiting time, average speed, occupancy and density were produced.
Patek - A collection of reusable pyspark utility functions that help make development easier!