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DataPipeLine-S3-to-Redshift-Using-Airflow

Overview

In this project, we will load divvy trips dataset present in S3 to AWS redshift with the help of Airflow. Divvy is Chicago's bike share system that provides a fast and convenient way to get around without having your own bike.

At first we need to finish below tasks

  • Build Airflow and Postgres image on docker windows using docker-compose.
  • Create Redshift cluster using AWS console.
  • upload divvy trips dataset files Link to files(pick any year) to S3 bucket. my bucket location is s3://udacity-dend/data-pipelines

Step 1

Build Airflow image using docker-compose up command

Open Airflow GUI, you will see something like this about datapipeline.

Step 2

Create AWS Redshift cluster using AWS console.

Cluster Details.

Add cluster permission to read S3 buckets. I have created redshifts3 role as shown below.

final cluster details

Step 3

Add AWS and Redshift credentials in Airflow. In aws_credentials, login is your Iam user access key id and password is secret access key.

Host is cluster end-point

Step 4

Switch on the dags in Airflow and verify tables are created in AWS redshift or not.

As you see our divvy trips tables are created in redshift as per sql_statements file which is present in /dags/modules folder

Step 5

Verfiy the data loaded in redshift tables.