etl-pipelines
There are 16 repositories under etl-pipelines topic.
yobix-ai/extractous
Fast and efficient unstructured data extraction. Written in Rust with bindings for many languages.
patterns-app/patterns-devkit
Data pipelines from re-usable components
level-vc/useful
The open-source Useful SDK. One python decorator in the Useful library allows for full observability of Python functions within an ETL.
Chek0rrdn/DataEngineer_ETL
A project structure for doing and sharing data engineer work.
abrahamkoloboe27/Airflow-Pipeline-Dashboard-Compagnie-Aerienne
Lien de l'application
angelxd84130/Airflow-ETL
Build ETL piplines on AirFlow to load data from BigQuery and store it in MySQL
ChristianRCanlas/ChristianRCanlas.github.io
e-Portfolio showcasing my personal projects.
EmmanuelEzenwere/DataSift
DataSift auto applies a data pre-processing pipeline to Data Science Projects.
prneidhardt/Apache-Data-Pipeline
Sparkify project
extralo/loom
Weaving together different threads (services like image/audio converse, ETL services, etc.) to enable the World Wide Flow
Guilherme-B/baboon
JSON-driven ETL pipeline framework prototype
siddarthaThentu/Disaster-Response-Pipeline
A deployed machine learning model that has the capability to automatically classify the incoming disaster messages into related 36 categories. Project developed as a part of Udacity's Data Science Nanodegree program.
speedbits/LimitlessETL
A Python and Spark based ETL framework. While it operates within speed limits that is framework and standards, but offers boundless possibilities.
juniors90/PymaciesArg
An extension that registers all pharmacies in Argentina.
omar-elmaria/airflow_local
This repo contains the DAGs that run on my local Airflow environment. I use the local environment to test my DAGs before deploying them to virtual machines via Kubernetes
SayamAlt/Formula-1-Data-Ingestion-Transformation---ETL-Pipeline
This project demonstrates a complete ETL pipeline for Formula 1 racing data using Azure Databricks, Delta Lake, and Azure Data Factory. It covers data ingestion, transformation with PySpark and Spark SQL, data governance with Unity Catalog, and visualization through Power BI. Designed to showcase real-world data engineering workflows in Azure.