Amazon Web Services Data-Engineer-Associate Exam With Confidence Using Practice Dumps

Problem Analysis:

The company uses AWS Glue for ETL pipelines and requires automatic data quality checks during pipeline execution.

The solution must integrate with existing AWS Glue pipelines and evaluate data quality rules based on predefined thresholds.

Key Considerations:

Ensure minimal implementation effort by leveraging built-in AWS Glue features.

Use a standardized approach for defining and evaluating data quality rules.

Avoid custom libraries or external frameworks unless absolutely necessary.

Solution Analysis:

Option A: SQL Transform

Adding SQL transforms to define and evaluate data quality rules is possible but requires writing complex queries for each rule.

Increases operational overhead and deviates from Glue's declarative approach.

Option B: Evaluate Data Quality Transform with DQDL

AWS Glue provides a built-in Evaluate Data Quality transform.

Allows defining rules in Data Quality Definition Language (DQDL), a concise and declarative way to define quality checks.

Fully integrated with Glue Studio, making it the least effort solution.

Option C: Custom Transform with PyDeequ

PyDeequ is a powerful library for data quality checks but requires custom code and integration.

Increases implementation effort compared to Glue's native capabilities.

Option D: Custom Transform with Great Expectations

Great Expectations is another powerful library for data quality but adds complexity and external dependencies.

Final Recommendation:

Use Evaluate Data Quality transform in AWS Glue.

Define rules in DQDL for checking thresholds, null values, or other quality criteria.

This approach minimizes development effort and ensures seamless integration with AWS Glue.

AWS Glue Data Quality Overview

DQDL Syntax and Examples

Glue Studio Transformations

The data engineer needs to detect custom categories of PII within the data lake using AWS Glue DataBrew. While DataBrew provides standard data quality rules, the solution must support custom PII categories.

Option B: Implement custom data quality rules in DataBrew. Apply the custom rules across datasets.This option is the most efficient because DataBrew allows the creation of custom data quality rules that can be applied to detect specific data patterns, including custom PII categories. This approach minimizes operational overhead while ensuring that the specific privacy requirements are met.

Options A, C, and D either involve manual intervention or developing custom scripts, both of which increase operational effort compared to using DataBrew's built-in capabilities.