Pipeline Consists of various modules:
- GoodReads Python Wrapper
- ETL Jobs
- Redshift Warehouse Module
- Analytics Module
Data is captured in real time from the goodreads API using the Goodreads Python wrapper (View usage - Fetch Data Module). The data collected from the goodreads API is stored on local disk and is timely moved to the Landing Bucket on AWS S3. ETL jobs are written in spark and scheduled in airflow to run every 10 minutes.
I have written detailed instruction on how to setup Airflow using AWS CloudFormation script. Check out - Airflow using AWS CloudFormation
NOTE: This setup uses EC2 instance and a Postgres RDS instance. Make sure to check out charges before running the CloudFromation Stack.
Project uses sshtunnel to submit spark jobs using a ssh connection from the EC2 instance. This setup does not automatically install sshtunnel for apache airflow. You can install by running below command:
pip install apache-airflow[sshtunnel]
Finally, copy the dag and plugin folder to EC2 inside airflow home directory.
Spinning up EMR cluster is preety straight forward. You can use AWS Guide available here.
ETL jobs in the project uses psycopg2 to connect to Redshift cluster to run staging and warehouse queries. To install psycopg2 on EMR:
sudo pip-3.6 install psycopg2
psycopg2 uses postgresql-devel and postgresql-libs, and sometimes pscopg2 installation may fail if these dependencies are not available. To install run commands:
sudo yum install postgresql-libs
sudo yum install postgresql-devel
ETL jobs also use boto3 move files between s3 buckets. To install boto3 run:
pip-3.6 install boto3 --user
Finally, pyspark uses python2 as default setup on EMR. To change to python3, setup environment variables:
export PYSPARK_DRIVER_PYTHON=python3
export PYSPARK_PYTHON=python3
Copy the ETL scripts to EMR and we have our EMR ready to run jobs.
You can follow the AWS Guide to run a Redshift cluster or alternatively you can use Redshift_Cluster_IaC.py Script to create cluster automatically.
