AWS Glue DataBrew for No-Code Data Transformation

SAA-C03 By islever Editorial Last updated

Determine appropriate data security controls.

Short answer

C Configure AWS Glue DataBrew to transform the data and share transformation steps using DataBrew recipes.

Question

A company hosts a data lake on Amazon S3. The data lake ingests data in Apache Parquet format from various data sources. The company uses multiple transformation steps to prepare the ingested data. The steps include filtering of anomalies, normalizing of data to standard date and time values, and generation of aggregates for analyses. The company must store the transformed data in S3 buckets that data analysts access. The company needs a prebuilt solution for data transformation that does not require code. The solution must provide data lineage and data profiling. The company needs to share the data transformation steps with employees throughout the company. Which solution will meet these requirements?
  1. Configure an AWS Glue Studio visual canvas to transform the data. Share the transformation steps with employees by using AWS Glue jobs.
  2. Configure Amazon EMR Serverless to transform the data. Share the transformation steps with employees by using EMR Serverless jobs.
  3. Configure AWS Glue DataBrew to transform the data. Share the transformation steps with employees by using DataBrew recipes. Correct Answer
  4. Create Amazon Athena tables for the data. Write Athena SQL queries to transform the data. Share the Athena SQL queries with employees.

Key Takeaways

The exam tests knowledge of managed data preparation services; the trap is confusing general ETL (Glue Studio) with specialized no-code profiling tools (DataBrew).

This question evaluates the selection of AWS Glue DataBrew as a visual, no-code data preparation tool that handles profiling and lineage. It establishes why this service is superior to code-based alternatives for non-technical users needing reusable transformation steps.

Common mistake: Candidates often choose AWS Glue Studio because it is part of the same suite, failing to realize that DataBrew is the specific solution designed for visual, no-code interaction and recipe sharing.

Expert Analysis

Why the Answer Is Correct

AWS Glue DataBrew is a visual data preparation tool that allows analysts to clean and normalize data without writing any code. It specifically provides built-in features for data profiling (analyzing data quality) and data lineage (tracking data origins), which are explicit requirements in the scenario. The 'recipes' feature allows these transformation steps to be saved, versioned, and shared easily among employees throughout the company.

Why the Other Options Are Wrong

Option A (Glue Studio) requires coding or complex visual job creation but lacks the dedicated 'recipe' sharing model and out-of-the-box data profiling capabilities of DataBrew. Option B (EMR Serverless) is a compute engine that requires writing PySpark/Scala code, violating the 'no code' requirement. Option D (Athena) is for querying data, not for transforming and storing transformed datasets via a visual workflow, and it does not provide data lineage or profiling natively.

Community Comment Notes

Community consensus strongly supports C, with multiple commenters noting that DataBrew is the only tool that combines no-code interaction with data profiling. As one user noted, "DataBrew is a visual data preparation tool that allows you to clean and normalize data without writing code," highlighting its suitability for non-technical staff. Another commenter pointed out that Athena "lacks data lineage, data profiling, and the ability to reuse transformations," confirming why D is incorrect.

Official Reference

https://docs.aws.amazon.com/databrew/latest/dg/what-is.html

This link opens the official vendor documentation in a new tab.

Exam Strategy

When a question mentions 'no code', 'visual', 'data profiling', or 'non-technical users', immediately look for AWS Glue DataBrew. If the requirement involves large-scale ETL with custom logic, consider Glue Studio or EMR instead.

Frequently Asked Questions

Why not use AWS Glue Studio for visual transformation?

Glue Studio is primarily for creating ETL jobs that may require code or complex mapping. It lacks the dedicated data profiling dashboard and simple 'recipe' sharing mechanism that DataBrew offers for non-technical analysts.

Does AWS Glue DataBrew support Apache Parquet?

Yes, DataBrew supports various input formats including CSV, JSON, Parquet, and Avro, making it suitable for the described data lake ingestion pipeline.

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. Scheldon 👍 4 Answered C
    AnswerC AWS Glue DataBrew is a visual data preparation tool that enables users to clean and normalize data without writing any code. Using DataBrew helps reduce the time it takes to prepare data for analytics and machine learning (ML) by up to 80 percent, compared to custom developed data preparation. You can choose from over 250 ready-made transformations to automate data preparation tasks, such as filtering anomalies, converting data to standard formats, and correcting invalid values. https://docs.aws.amazon.com/databrew/latest/dg/what-is.html
  2. Lingo43 👍 2 Answered C
    AWS Glue DataBrew: This is a visual data preparation tool that allows you to clean and normalize data without writing code. It has built-in transformations for common tasks like filtering anomalies, normalizing dates, and generating aggregates. It also provides data lineage and profiling capabilities, which are required by the company. DataBrew Recipes: These are reusable workflows that define the data transformation steps. They can be easily shared with other employees, making it simple to collaborate on data preparation tasks.
  3. LeonSauveterre 👍 2 Answered C
    A - AWS Glue doesn't focus on data profiling or provide an easy way for non-technical employees to collaborate on transformation steps. B - You need to code. C - AWS Glue DataBrew is a no-code tool specifically designed for data preparation and transformation. D - Let's say writing SQLs are not coding, still, Athena lacks data lineage, data profiling, and the ability to reuse transformations as recipes.
  4. Linuslin 👍 1 Answered C
    C is correct. https://docs.aws.amazon.com/databrew/latest/dg/recipes.html
  5. asdfcdsxdfc 👍 4 Answered C
    Should be C
Show 1 more comment
  1. seetpt 👍 2 Answered C
    Agree with C

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