Least Refactoring Airflow Migration to AWS

Orchestrate data pipelines.

Answer Correct answer: C — Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow.

A company uses Apache Airflow to orchestrate the company's current on-premises data pipelines. The company runs SQL data quality check tasks as part of the pipelines. The company wants to migrate the pipelines to AWS and to use AWS managed services. Which solution will meet these requirements with the LEAST amount of refactoring?

Setup AWS Outposts in the AWS Region that is nearest to the location where the company uses Airflow. Migrate the servers into Outposts hosted Amazon EC2 instances. Update the pipelines to interact with the Outposts hosted EC2 instances instead of the on-premises pipelines.
Create a custom Amazon Machine Image (AMI) that contains the Airflow application and the code that the company needs to migrate. Use the custom AMI to deploy Amazon EC2 instances. Update the network connections to interact with the newly deployed EC2 instances.
Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow. Correct Answer
Convert the pipelines to AWS Step Functions workflows. Recreate the data quality checks in SQL as Python based AWS Lambda functions.

Community Votes

100%

100% of anonymous learners picked answer C. Votes are pick records left by other test-takers — they are not the verified answer.

Community Insight

The question tests knowledge of managed orchestration services; the trap is choosing custom EC2 deployments or rewriting logic in Step Functions, which increase refactoring effort significantly compared to migrating directly to MWAA.

Migrating on-premises Apache Airflow pipelines to AWS with minimal refactoring requires using Amazon Managed Workflows for Apache Airflow (MWAA). This managed service allows existing configurations and SQL-based data quality checks to run with little to no code changes.

Candidates often select Option B (custom AMI on EC2) because it seems like a direct lift-and-shift, but managing Airflow components manually on EC2 violates the 'AWS managed services' requirement and increases operational overhead compared to the fully managed MWAA.

Community Discussion (3 comments)

bakarys 👍 3 Selected: C

The solution that will meet these requirements with the least amount of refactoring is Option C: Migrate the existing Airflow orchestration configuration into Amazon Managed Workflows for Apache Airflow (Amazon MWAA). Create the data quality checks during the ingestion to validate the data quality by using SQL tasks in Airflow. Amazon Managed Workflows for Apache Airflow (MWAA) is a fully managed service that makes it easy to run open-source versions of Apache Airflow on AWS. It allows you to build workflows to design and visualize pipelines, automate complex tasks, and monitor executions. Since the company is already using Apache Airflow for orchestration, migrating to Amazon MWAA would require minimal refactoring.

HunkyBunky 👍 3 Selected: C

Amazon MWAA - becuase we already uses Apache Airflow

tgv 👍 3 Selected: C

Amazon MWAA is a managed service for running Apache Airflow. It allows migrating existing Airflow configurations with minimal changes. Data quality checks can continue to be implemented as SQL tasks in Airflow, similar to the current setup.

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Expert Analysis

Why the Answer Is Correct

Option C is the correct choice because Amazon Managed Workflows for Apache Airflow (MWAA) is a fully managed service designed specifically to run Apache Airflow. Since the company already uses Airflow, migrating the DAGs and configuration files to MWAA requires the least amount of refactoring. The SQL data quality checks can continue to run as they are, leveraging Airflow's native capabilities without needing to rewrite logic into other AWS services.

Why the Other Options Are Wrong

Option A (Outposts) and Option B (EC2 with custom AMI) involve running Airflow on compute instances rather than using a managed orchestration service, which contradicts the requirement to use AWS managed services for the pipeline orchestration. Option D suggests converting pipelines to AWS Step Functions and rewriting SQL tasks as Python Lambda functions. This represents a significant refactoring effort, changing the orchestration engine and the implementation language of the tasks, which is the opposite of the 'least refactoring' goal.

Community Comment Notes

Community consensus strongly supports Option C. As user HunkyBunky noted, "Amazon MWAA - becuase we already uses Apache Airflow," highlighting that leveraging existing infrastructure reduces migration complexity. User tgv added that "Amazon MWAA is a managed service... allows migrating existing Airflow configurations with minimal changes," reinforcing that this is the standard path for lift-and-shift migrations of Airflow workloads.

Exam Strategy

When a question asks for the 'least refactoring' or 'minimal changes' during migration, look for the managed service that natively supports the existing open-source technology. Avoid options that suggest rewriting code or switching to entirely different architectural patterns unless explicitly required by constraints not met by the managed service.

Frequently Asked Questions

Why not use EC2 with a custom AMI for Airflow?

While EC2 allows you to run Airflow, it is not a managed orchestration service. Managing the Airflow backend (metadata DB, scheduler) yourself increases operational burden and doesn't align with the 'managed services' preference for least refactoring.

Can SQL tasks run in Amazon MWAA?

Yes. Amazon MWAA supports all standard Airflow operators and hooks, including SQL-based tasks. You can reuse your existing SQL queries within the DAGs without rewriting them into Python or other languages.

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