Guide

MongoDB to Redshift with Apache Airflow


In this guide, we’ll explore how you can use Apache Airflow to move your data from your MongoDB to Redshift. Note that this is an effective and flexible alternative to point-and-click ETL tools like Segment, Alooma, Xplenty, Stitch, and ETLeap.

Before we get started, be sure you have the following on hand:

  • A MongoDB account
  • An S3 bucket with a valid aws_access_key_id and aws_secret_access_key
  • A Redshift instance with a valid host IP and login information
  • An instance of Apache Airflow. You can either set this up yourself if you have devops resources or sign up and get going immediately with Astronomer’s managed Airflow service. Note that this guide will use commands using the Astronomer CLI to push dags into production and will assume you’ve spun up an Airflow instance via Astronomer, but the core code should work the same regardless of how you’re hosting Airflow
  • Docker running on your machine

This DAG uses a Mongo collection processing script that accepts a json formatted Mongo schema mapping and outputs both a Mongo query projection and a compatible Redshift schema mapping. This script can be found here.This DAG also contains a flattening script that removes invalid characters from the Mongo keys as well as scrubbing out the "_$date" suffix that PyMongo appends to datetime fields.

1. Add Connections in Airflow UI

Begin by creating all of the necessary connections in your Airflow UI. To do this, log into your Airflow dashboard and navigate to Admin-->Connections. In order to build this pipeline, you’ll need to create a connection to your MongoDB account, your S3 bucket, and your Redshift instance. For more info on how to fill out the fields within your connections, check out our documentation here.

2. Clone the plugin

If you haven't done so already, navigate into your project directory and create a plugins folder by running mkdir plugins in your terminal. Navigate into this folder by running cd plugins and clone the Github Plugin using the following command:

git clone https://github.com/airflow-plugins/mongo_plugin.git

This will allow you to use the Mongo hook to establish a connection to Mongo and extract data into a file. You will also be able to use the appropriate operators to transfer the Mongo data to S3 and then from S3 to Redshift.

3. Copy the DAG file

Navigate back into your project directory and create a dags folder by running mkdir dags. Copy the Mongo to Redshift DAG file into this folder.

4. Customize

Open up the mongo_to_redshift.py file from the repo you just cloned in a text editor of your choice and input the following credentials in lines 35-40:

S3_CONN = ''
S3_BUCKET = ''
REDSHIFT_SCHEMA = ''
REDSHIFT_CONN_ID = ''
MONGO_CONN_ID = ''
MONGO_DATABASE = ''

5. Test + Deploy

Once you have those credentials plugged into your DAG, test and deploy it!

If you don't have Airflow already set up in your production environment, head over to our app to get spun up with your own managed instance!


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