Orchestrating AWS Glue DataBrew with Step Functions

SAA-C03 By islever Editorial Last updated

Design scalable and loosely coupled architectures.

Short answer

C Use AWS Glue DataBrew for data preparation and AWS Step Functions to orchestrate the jobs, handling state, ordering, and retries automatically.

Question

A company's marketing data is uploaded from multiple sources to an Amazon S3 bucket. A series of data preparation jobs aggregate the data for reporting. The data preparation jobs need to run at regular intervals in parallel. A few jobs need to run in a specific order later. The company wants to remove the operational overhead of job error handling, retry logic, and state management. Which solution will meet these requirements?
  1. Use an AWS Lambda function to process the data as soon as the data is uploaded to the S3 bucket. Invoke other Lambda functions at regularly scheduled intervals.
  2. Use Amazon Athena to process the data. Use Amazon EventBridge Scheduler to invoke Athena on a regular internal.
  3. Use AWS Glue DataBrew to process the data. Use an AWS Step Functions state machine to run the DataBrew data preparation jobs. Correct Answer
  4. Use AWS Data Pipeline to process the data. Schedule Data Pipeline to process the data once at midnight.

Key Takeaways

The core trap is confusing compute services (Lambda/Athena) with orchestration services; you must identify that Step Functions provides the required state management, retry logic, and control flow without operational overhead.

This question tests the selection of a managed workflow orchestration service to handle parallel execution, specific ordering, and error handling for data preparation jobs. The correct solution leverages AWS Step Functions to manage state and integrate with AWS Glue DataBrew.

Common mistake: Many candidates incorrectly choose Lambda or Athena, failing to realize that while these are compute/query engines, they lack native built-in capabilities for complex workflow sequencing, distributed state management, and automatic retry policies across multiple distinct steps.

Expert Analysis

Why the Answer Is Correct

Option C is the only solution that fully meets the requirements for removing operational overhead regarding error handling, retry logic, and state management. AWS Glue DataBrew is a serverless data preparation service ideal for marketing data aggregation. By using an AWS Step Functions state machine to invoke DataBrew jobs, you gain native support for parallel execution branches and sequential steps. Step Functions automatically handles state persistence, retries on failure, and error catching, eliminating the need for custom code.

Why the Other Options Are Wrong

Option A is incorrect because managing parallel jobs, order dependencies, and retry logic in Lambda requires significant custom orchestration code, increasing operational overhead. Option B is incorrect because Amazon Athena is a query service, not a workflow orchestrator; it does not natively support complex dependency graphs or distributed job chaining. Option D is incorrect because AWS Data Pipeline is an older, more rigid service that often requires manual configuration for error handling and is generally considered outdated compared to the modern serverless combination of Step Functions and Glue.

Community Comment Notes

The community consensus strongly favors Option C. As one commenter noted, "Glue DataBrew simplifies the data preparation process... and integrates with S3." Another user highlighted that "Step Functions' built-in controls... make sure that your application runs in order and as expected," confirming the value of managed state. Comments also emphasize that Data Pipeline (Option D) is "outdated" and Lambda (Option A) is "incompetent for retries" in this context without extensive coding.

Official Reference

These links open the official vendor documentation in a new tab.

Exam Strategy

When a question mentions 'state management', 'retry logic', and 'specific order' for workflows, immediately think of AWS Step Functions. It is the primary managed service for orchestrating distributed applications and microservices, decoupling the logic from the compute resources.

Frequently Asked Questions

Why can't I use Lambda for all the data preparation jobs?

Lambda lacks native distributed state management and complex dependency ordering. You would have to write custom code to track job states and handle retries, which increases operational overhead.

Is AWS Data Pipeline still relevant for new projects?

No, AWS Data Pipeline is largely legacy. For modern ETL and data preparation workflows, AWS Glue combined with Step Functions offers a more flexible, serverless, and managed alternative.

More SAA-C03 FAQ →

Community Votes

C
100%

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

Discussion 6 contributions

Compiled from public community threads and reader submissions, lightly edited for clarity. Handles and vote tallies are shown as recorded, and comments reflect the views of individual contributors.

From public community threads 6

  1. [Removed] 👍 12 Answered C
    data preparation = Glue DataBrew https://docs.aws.amazon.com/databrew/latest/dg/what-is.html state handling = DataBrew with Step Functions https://docs.aws.amazon.com/step-functions/latest/dg/connect-databrew.html
  2. Scheldon 👍 3 Answered C
    AnswerC With Step Functions' built-in controls, you can examine the state of each step in your workflow to make sure that your application runs in order and as expected. https://docs.aws.amazon.com/step-functions/latest/dg/welcome.html AWS Glue is a serverless data integration service that makes it easy for analytics users to discover, prepare, move, and integrate data from multiple sources. https://docs.aws.amazon.com/glue/latest/dg/what-is-glue.html
  3. LeonSauveterre 👍 2 Answered C
    A - Lambda function alone is incompetent for retries or running in a specific order. B - Athena cannot automatically handle errors or running in a specific order. C - YES and fully managed. See below. D - Data Pipeline with retries and error handling is A LOT OF WORK. Takeaway: 1. Glue DataBrew simplifies the data preparation process with a visual interface, and it integrates with S3 for input/output. 2. Step Functions efficiently manages parallel and sequential workflows while minimizing operational overhead for state management, error handling, and retries.
  4. FlyingHawk 👍 1 Answered C
    Both c and d are workable, but AWS Data Pipeline is considered outdated because AWS has introduced newer, more efficient, and serverless alternatives for data orchestration and ETL (Extract, Transform, Load) tasks. While AWS Data Pipeline is still available, it is not actively promoted or updated by AWS, and many companies have shifted to AWS Glue, Step Functions, and EventBridge for data workflow automation. https://aws.amazon.com/blogs/big-data/build-event-driven-data-quality-pipelines-with-aws-glue-databrew/
  5. Andy_09 👍 9
    Option C
Show 1 more comment
  1. asdfcdsxdfc 👍 3 Answered C
    c looks correct

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