Implementing Automated Data Quality Checks with AWS Glue DataBrew

DEA-C01 By islever Editorial Last updated

Ensure data quality. Transform and process data.

Short answer

A Use AWS Glue DataBrew to define completeness rules for timestamps and numeric range validation for temperature values with minimal coding.

Question

A manufacturing company uses AWS Glue jobs to process IoT sensor data to generate predictive maintenance models. A data engineer needs to implement automated data quality checks to identify temperature readings that are outside the expected range of -50°C to 150°C. The data quality checks must also identify records that are missing timestamp values.
The data engineer needs a solution that requires minimal coding and can automatically flag the specified issues.
Which solution will meet these requirements?
  1. Create an AWS Glue DataBrew project to profile the sensor data Define completeness rules for timestamps. Set up numeric range validation for temperature values. Correct Answer
  2. Use AWS Glue’s Data Quality rules and machine learning (ML)-based anomaly detection to identify missing timestamps and to detect temperature anomalies.
  3. Create an AWS Lambda function to scan the sensor data files to validate temperature ranges. Use AWS Glue Data Catalog tables to check timestamp completeness.
  4. Create an AWS Glue DynamicFrame that uses a custom data quality operator to profile the sensor data. Use Amazon SageMaker Data Wrangler transforms to validate timestamps and temperature ranges.

Key Takeaways

The question tests the ability to choose a low-code data preparation tool (DataBrew) over custom scripting or complex ML solutions when simple rule-based validation is sufficient.

Learn how to perform automated data quality checks on IoT sensor data using AWS Glue DataBrew with minimal coding, focusing on completeness and range validation.

Common mistake: Choosing option B because machine learning sounds advanced, but it ignores the 'minimal coding' constraint and adds unnecessary operational overhead for simple range checks.

Expert Analysis

The scenario requires implementing automated data quality checks for IoT sensor data processed by AWS Glue, specifically targeting two issues: temperature values outside a defined range (-50°C to 150°C) and missing timestamp fields. The critical constraint is that the solution must require minimal coding.

AWS Glue DataBrew is a visual data preparation tool designed for exactly this use case. It allows users to create recipes to clean, normalize, and enrich data without writing code. DataBrew supports built-in transforms for handling missing values (completeness checks) and conditional logic or filters to identify outliers or values outside specific ranges. This aligns perfectly with the requirement for minimal coding while providing robust data profiling and validation capabilities.

Option B suggests using ML-based anomaly detection. While powerful, this approach is complex, requires significant setup, training, and maintenance overhead, violating the 'minimal coding' and simplicity constraints. Option C involves creating a custom Lambda function, which directly contradicts the 'minimal coding' requirement as it demands manual script development. Option D combines Glue DynamicFrames with SageMaker Data Wrangler, introducing unnecessary architectural complexity and additional services not required for straightforward rule-based validation.

Therefore, leveraging AWS Glue DataBrew's native capabilities for completeness and range validation is the most efficient and appropriate solution.

Exam Strategy

When questions emphasize 'minimal coding', 'visual interface', or 'no-code/low-code' in the context of data transformation or cleaning, prioritize AWS Glue DataBrew. Avoid options involving custom Lambda functions (high code) or SageMaker/ML (high complexity/setup) unless the problem explicitly requires predictive modeling or pattern recognition beyond simple rules.

Frequently Asked Questions

Why is AWS Glue DataBrew preferred over Lambda for data quality?

DataBrew provides a visual interface and pre-built transforms for common data quality tasks like filtering and null-checking, significantly reducing the amount of code compared to writing custom scripts in Lambda.

Can DataBrew handle missing value detection automatically?

Yes, DataBrew includes specific actions and profiles to detect and handle missing values, allowing you to flag or fill them without manual programming.

More DEA-C01 FAQ →

Community Votes

A
75%
B
25%

75% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 4 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 4

  1. learner2023 👍 2 Answered A
    AWS Glue DataBrew is purpose-built for data quality checks with minimal coding—completeness rules for missing timestamps and numeric range validation for temperature values (custom range -50°C to 150°C) automatically flag issues without requiring custom code or ML setup. (B requires ML setup overhead. C requires custom Lambda coding. D adds unnecessary complexity with SageMaker.)
  2. TrainingCA05 👍 2 Answered A
    minimal coding, less setup overhead
  3. Varun1Bisht 👍 2 Answered B
    It should be B
  4. AWS82siast 👍 1 Answered A
    Minimal Coding

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