patterns-devkit
pyspark-example-project
| patterns-devkit | pyspark-example-project | |
|---|---|---|
| 5 | 1 | |
| 106 | 1,370 | |
| 0.0% | - | |
| 2.9 | 0.0 | |
| about 1 year ago | over 1 year ago | |
| Python | Python | |
| BSD 3-clause "New" or "Revised" License | - |
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patterns-devkit
pyspark-example-project
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Learning Pyspark for a new role
https://github.com/AlexIoannides/pyspark-example-project You can use this as an example to organize your project. I have referred to this in the past.
What are some alternatives?
Dataplane - Dataplane is a data platform that makes it easy to construct a data mesh with automated data pipelines and workflows.
soda-spark - Soda Spark is a PySpark library that helps you with testing your data in Spark Dataframes
pipebird - Pipebird is open source infrastructure for securely sharing data with customers.
Apache-Spark-Guide - Apache Spark Guide
SmartPipeline - A framework for rapid development of robust data pipelines following a simple design pattern
hamilton - Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage and metadata. Runs and scales everywhere python does.
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
AWS Data Wrangler - pandas on AWS - Easy integration with Athena, Glue, Redshift, Timestream, Neptune, OpenSearch, QuickSight, Chime, CloudWatchLogs, DynamoDB, EMR, SecretManager, PostgreSQL, MySQL, SQLServer and S3 (Parquet, CSV, JSON and EXCEL).
TypedPyspark - Type-annotate your spark dataframes and validate them
flowrunner - Flowrunner is a lightweight package to organize and represent Data Engineering/Science workflows
dados-censup - Automação da ingestão de dados disponibilizados pelo INEP referente ao censo superior da educacão brasileira.