Data Workflows with GCP Dataproc, Apache Airflow and Apache Spark
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Updated
Mar 4, 2020 - Python
Data Workflows with GCP Dataproc, Apache Airflow and Apache Spark
Building Data Pipelines for a data warehouse with Airflow and AWS
Get started with Apache Airflow. Check the README for instructions on how to run your first DAGs today. 🚀
Udacity data engineering nanodegree projects using song logs dataset
Common functionality for implementing data flows in Apache Airflow DAGs.
A end to end data analytics work that consumes data from AWS S3, executing ETL process and creating datamart on Snowflake following to that a PowerBI report created by using datamart
Data Pipeline Project
ETL Pipeline for Spotify API on Airflow
Welcome to my Apache Airflow learning journey repository! 🚀 This repository serves as a comprehensive documentation of my exploration and understanding of Apache Airflow, an open-source platform for orchestrating complex workflows.
Basics to Advanced airflow concepts
Airflow DAG tutorial with docker compose local setup
Airflow operators, hooks, and sensors for interacting with the Hightouch API
Apache Airflow demo project that setup 3 DAGs to explain how to pass parameters from a DAG to a triggered DAG.
Creating pipelines using Python3 and Apache Airflow to load tables into Google Big Query Dataware House
Airflow Pipline to extract data from twitter API then transform it and finally load it to google cloud storage bucket.
Data Pipeline Analytics Platform is an end-to-end generic Big Data pipeline. Involves following tech stack: AWS S3, AWS Redshift, AWS EMR Cluster, Apache Spark, Apache Airflow.
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