Source code of the Apache Airflow Tutorial for Beginners on YouTube Channel Coder2j (https://www.youtube.com/c/coder2j)
-
Updated
Feb 27, 2024 - Python
Source code of the Apache Airflow Tutorial for Beginners on YouTube Channel Coder2j (https://www.youtube.com/c/coder2j)
Airflow Pipeline for Machine Learning
🎵 LyricWave – AI Music Composer (Proof of Concept) 🎶 A personal project exploring automatic generation of unique MP4 songs. LyricWave blends lyrics with AI-generated melodies and synthetic vocals to experiment with new forms of musical expression. A creative testbed to push your ideas into sound. 🚀🎧
An open-source project dedicated to constructing robust data pipelines and scalable software infrastructure. We leverage industry-standard tools favored by developers to enhance efficiency and reliability. Uniquely, these pipelines are field-tested on farms across Sumatra, Indonesia, ensuring real-world applicability and resilience.
My self-learning about Apache Airflow
This is my Apache Airflow Local development setup on Windows 10 WSL2/Mac using docker-compose. It will also include some sample DAGs and workflows.
Repo for building docker based airflow image. Containers support multiple features like writing logs to local or S3 folder and Initializing GCP while container booting. https://abhioncbr.github.io/docker-airflow/
Apache Airflow Guide
An End-to-End ETL data pipeline that leverages pyspark parallel processing to process about 25 million rows of data coming from a SaaS application using Apache Airflow as an orchestration tool and various data warehouse technologies and finally using Apache Superset to connect to DWH for generating BI dashboards for weekly reports
From data gathering to model deployment. Complete ML pipeline using Docker, Airflow and Python.
Building Data Warehouse on BigQuery which takes flat file as the data sources with Airflow as the Orchestrator
An airflow deployment configuration with sane defaults
In this project, you will be building a Twitter Scheduler using Apache Airflow on Docker.
A starting point for a data stack using Python, Apache Airflow and Metabase.
Run Apache Airflow using Docker containers
Tools to streamline Jupyter Notebook Prototypes into robust Data Products
Automated Indeed Job Offer Scraper: Airflow Orchestrated and Scheduled, Data Loaded into PostgreSQL Database
Shares ETL to scrape historical data of one shares and import in metabase
This documentation provides an overview of the tasks completed in this project. The project comprises three key tasks: ETL (Extract, Transform, Load), API development, and Data Orchestration using Airflow. Each task is detailed below along with explanations of design choices and considerations.
Airflow docker boiler plate with LocalExecutor, CeleryExecutor. #TODO Kubernetes
Add a description, image, and links to the airflow-docker topic page so that developers can more easily learn about it.
To associate your repository with the airflow-docker topic, visit your repo's landing page and select "manage topics."