Cloud Composer (Apache Airflow) ➔ Amazon MWAA & AWS Step Functions
From Cloud Composer (Managed Airflow on GKE) to Amazon MWAA (Managed Airflow on AWS) & AWS Step Functions.
If you want to keep your Python Apache Airflow DAGs, **Amazon MWAA (Managed Workflows for Apache Airflow)** is the exact 1-to-1 equivalent to Cloud Composer! If you want a 100% serverless, zero-maintenance orchestrator with visual execution graphs, use **AWS Step Functions**.
1. Architectural Mechanism Comparison
Cloud Composer (Apache Airflow)
Managed Apache Airflow running on GKE where 100% of pipeline DAGs, operators, and task dependencies are authored in Python code.
- Complete programmatic flexibility using Python code and custom operators.
- Rich Airflow open-source ecosystem, plugins, and custom hooks.
- Version-controlled Git workflows for all DAG definitions.
Amazon MWAA & AWS Step Functions
AWS provides two major orchestration paths: 1) Amazon MWAA (Managed Workflows for Apache Airflow): Exact 1-to-1 managed Apache Airflow on AWS, and 2) AWS Step Functions: Serverless visual state machines coordinating tasks with JSON-based Amazon States Language (ASL).
- Amazon MWAA: Seamless migration for existing Airflow DAGs with zero code rewrites.
- AWS Step Functions: 100% serverless ($0 idle cost) with visual execution graphs and native integration with 200+ AWS services.
- AWS Glue Workflows: Lightweight orchestration for native Glue ETL jobs and crawlers.
2. Interactive Terminology & Concept Bridge
Interactive Concept Bridge: Terminology & Architectural Mapping
Click any concept below to see how your GCP knowledge directly maps into AWS.
Cloud Composer (Managed Airflow)
Managed Apache Airflow running on Google Kubernetes Engine (GKE).
Amazon MWAA (Managed Airflow)
Managed Apache Airflow running on AWS Fargate/VPC.
Exact 1-to-1 match: both run standard open-source Apache Airflow with auto-scaling workers.
3. Visual Architecture Pipeline (Amazon MWAA & AWS Step Functions)
AWS Glue 3-Stage Architecture: Crawlers & Catalog ➔ Serverless Spark DPUs ➔ Curated Sinks
Click any section below or run the simulation to explore AWS Glue serverless Spark ETL and DynamicFrames.
2. Serverless Spark ETL & DynamicFrames
Executes distributed Spark ETL jobs billed in Data Processing Units (DPUs). Glue DynamicFrames resolve semi-structured nested data without failing, while Job Bookmarks prevent re-processing old files.
“Like a flexible assembly line of robots that seamlessly reshape raw materials, even when some parts arrive in unexpected irregular shapes.”
- Serverless DPUs: Auto-scales from 2 to 100+ DPUs (4 vCPUs + 16GB RAM per DPU) in seconds.
- DynamicFrames: Native schema-drift handling using `choice` types to prevent pipeline crashes.
- Job Bookmarks: Tracks processed S3 object state across repeated pipeline runs.
4. Side-by-Side Code, CLI & Terraform Translator
Side-by-Side Code & Syntax Translator
# Google Cloud Composer CLI Trigger
gcloud composer environments run my-airflow-env \
--location us-central1 \
dags trigger -- DailySalesETL# Amazon MWAA CLI Trigger (via AWS CLI)
aws mwaa create-cli-token --name MyMwaaEnv
# Execute using generated CLI web token:
# curl -X POST https://<env>.airflow.us-east-1.amazonaws.com/api/v1/dags/DailySalesETL/dagRuns5. Paradigm Shift Gotchas: Traps to Avoid in AWS
MWAA Private vs. Public Web Server Routing
Configuring an MWAA environment with Private Network access blocks developers from opening the Airflow web console from outside the VPC.
Choose **Public Network** mode with IAM login for simple browser access during development.
6. Test Your Mental Model
Quick Knowledge Check: Test Your AWS Mental Model
Solidify your cross-cloud understanding with instant feedback.