Apache Airflow for complex ETL workflows using AWS Glue

Created by DELLE

Let’s dive deeper into serverless computing and explore how we can integrate it with Apache Airflow for complex ETL workflows using AWS Glue. This combination allows us to create powerful, scalable data processing pipelines with minimal infrastructure management.

Serverless computing, at its core, is about abstracting away infrastructure management, allowing developers to focus on writing code that delivers business value. In the AWS ecosystem, this primarily involves services like Lambda, API Gateway, DynamoDB, and S3. However, when we start talking about big data processing and ETL (Extract, Transform, Load) workflows, AWS Glue enters the picture as a serverless ETL service.

Let’s explore how we can combine these serverless concepts with Apache Airflow to orchestrate complex data workflows. We’ll create a system that ingests data, processes it using Glue, and then loads it into a data warehouse.

First, let’s set up our Airflow environment using Amazon MWAA (Managed Workflows for Apache Airflow). We’ll use Terraform to define this infrastructure:

provider "aws" {
  region = "us-west-2"
}

resource "aws_mwaa_environment" "example" {
  name = "example-mwaa-environment"

  source_bucket_arn = aws_s3_bucket.airflow_bucket.arn

  execution_role_arn =…