F2-Data-Pipeline
covid-19-data-engineering-pipeline
F2-Data-Pipeline | covid-19-data-engineering-pipeline | |
---|---|---|
1 | 1 | |
8 | 22 | |
- | - | |
5.9 | 5.3 | |
9 months ago | 6 months ago | |
Python | Python | |
MIT License | MIT License |
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F2-Data-Pipeline
covid-19-data-engineering-pipeline
-
COVID-19 data pipeline on AWS feat. Glue/PySpark, Docker, Great Expectations, Airflow, and Redshift, templated in CF/CDK, deployable via Github Actions
I've seen amazing projects here already, which honestly were a great inspiration, and today I would like to show you my project. Some time ago, I had the idea to apply every tool I wanted to learn or try out to the same topic and since then this idea has grown into an entire pipeline: https://github.com/moritzkoerber/covid-19-data-engineering-pipeline
What are some alternatives?
ethereum-etl-airflow - Airflow DAGs for exporting, loading, and parsing the Ethereum blockchain data. How to get any Ethereum smart contract into BigQuery https://towardsdatascience.com/how-to-get-any-ethereum-smart-contract-into-bigquery-in-8-mins-bab5db1fdeee
awesome-chalice - Discover the power of AWS Chalice, the ultimate framework for crafting seamless Python serverless applications. With Chalice, you can effortlessly build and manage HTTPS APIs, create web apps using popular front-end toolkits, and serve as the backend for cross-platform desktop and mobile apps developed with Qt for Python.
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
dataall - A modern data marketplace that makes collaboration among diverse users (like business, analysts and engineers) easier, increasing efficiency and agility in data projects on AWS.
data_check - data and pipeline testing with and for SQL
lambda2docker - Generate a Dockerfile from an AWS Lambda function
great_expectations - Always know what to expect from your data.
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.
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
Patek - A collection of reusable pyspark utility functions that help make development easier!