DEND-Data_Pipeline_Airflow

Loading S3 file with Airflow to ETL with Redshift

The purpose of this project is to build an adapted data model thanks to python to load data in a JSON file format and wrangle them into a star schema (see the ERD) with the pipeline written as a code thanks to AirFlow.

Prerequisite

1. Install Docker.

2. This project is run with docker.

   docker run -d -p 8080:8080 -v /path/to/project/dags:/usr/local/airflow/dags -v /path/to/project/plugins:/usr/local/airflow/plugins -v to/project/requirements.txt:/requirements.txt --name airflow puckel/docker-airflow webserver
   

And everything is setup to launch AirFlow.

3. You need also to configure your AWS credential with the AirFlow IU:

   We'll use Airflow's UI to configure your AWS credentials and connection to Redshift.

   1. Go to the Airflow UI:

   2. Under Connections, select Create.

   3. On the create connection page, enter the following values:

      • Conn Id: Enter aws_credentials.
      • Conn Type: Enter Amazon Web Services.
      • Login: Enter your Access key ID from the IAM User credentials you downloaded.
      • Password: Enter your Secret access key from the IAM User credentials you downloaded.

      Once you've entered these values, select Save and Add Another.

Main Goal

The compagny Sparkify need to analyses theirs data to better know the way users (free/paid) use theirs services. With this data pipeline we will be able to schedule, monitor and build more easily the ETL of this data.

Data Pipeline

This data pipeline is easy to read and understand even for a newcomer to AirFlow.

Data Model

This pipeline finally is made to build this DB star schema below to make easier the data analysis

Run it

Few steps

Go to the AirFlow UI after several seconds after running your container.