Salesforce API to Redshift with Apache Airflow
In this guide, we’ll explore how you can use Apache Airflow to move your CRM data from Salesforce 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 Salesforce account
- An S3 bucket with a valid
- 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 plugin will allow you to pull the following Salesforce objects into your Redshift database:
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 Salesforce 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 Salesforce Plugin using the following command:
git clone https://github.com/airflow-plugins/salesforce_plugin.git
This will allow you to use the Salesforce hook to establish a connection to Salesforce and extract data into a file. You will also be able to use the appropriate operators to transfer the Salesforce 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 Salesforce to Redshift DAG file into this folder.
Open up the
salesforce_to_redshift.py file from the repo you just cloned in a text editor of your choice and input your Salesforce Connection ID, S3 Connection ID, S3 Bucket Name, Redshift Connection ID, Redshift Schema Name, Origin Schema, and Schema Location. Save the file once this information has been imported.
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 getting started guide to get spun up with your own managed instance!
Ready to build your data workflows with Airflow?
Astronomer is the data engineering platform built by developers for developers. Send data anywhere with automated Apache Airflow workflows, built in minutes...