Least Overhead Order Tracking Across Operational Systems

DEA-C01 By islever Editorial Last updated

Automate data processing by using AWS services.

Short answer

D Use AWS Database Migration Service to capture changed records and publish to Amazon DynamoDB, then build dashboards in Amazon QuickSight.

Question

An ecommerce company operates a complex order fulfilment process that spans several operational systems hosted in AWS. Each of the operational systems has a Java Database Connectivity (JDBC)-compliant relational database where the latest processing state is captured. The company needs to give an operations team the ability to track orders on an hourly basis across the entire fulfillment process. Which solution will meet these requirements with the LEAST development overhead?
  1. Use AWS Glue to build ingestion pipelines from the operational systems into Amazon Redshift Build dashboards in Amazon QuickSight that track the orders.
  2. Use AWS Glue to build ingestion pipelines from the operational systems into Amazon DynamoDBuild dashboards in Amazon QuickSight that track the orders.
  3. Use AWS Database Migration Service (AWS DMS) to capture changed records in the operational systems. Publish the changes to an Amazon DynamoDB table in a different AWS region from the source database. Build Grafana dashboards that track the orders.
  4. Use AWS Database Migration Service (AWS DMS) to capture changed records in the operational systems. Publish the changes to an Amazon DynamoDB table in a different AWS region from the source database. Build Amazon QuickSight dashboards that track the orders. Correct Answer

Key Takeaways

The exam tests the distinction between batch ETL (Glue/Redshift) and real-time CDC (DMS/DynamoDB), with the trap being that Redshift is not required when DynamoDB suffices for simple state tracking.

This question evaluates the optimal AWS architecture for tracking orders across multiple operational systems with minimal development effort. The correct solution leverages AWS DMS for change data capture and QuickSight for visualization.

Common mistake: Most candidates chose A, incorrectly assuming a full data warehouse (Redshift) was necessary because the source systems are relational databases (JDBC), overlooking the lower overhead of DynamoDB + DMS.

Expert Analysis

Why the Answer Is Correct

Option D is the correct answer because it addresses the 'LEAST development overhead' requirement most effectively. AWS Database Migration Service (DMS) supports Change Data Capture (CDC) natively for JDBC-compliant databases, allowing real-time replication of state changes to Amazon DynamoDB without writing custom ingestion code. Amazon QuickSight integrates directly with DynamoDB via Athena or native connectors, enabling rapid dashboard creation. This combination minimizes the need for building, testing, and maintaining complex ETL pipelines.

Why the Other Options Are Wrong

Option A suggests using AWS Glue and Amazon Redshift. While Redshift is powerful, setting up a full data warehouse pipeline involves significant configuration, schema design, and maintenance overhead compared to a NoSQL store like DynamoDB. Furthermore, the prompt asks for hourly tracking of processing states, which fits well within DynamoDB's key-value structure. Options B and C suggest using Grafana; while viable, integrating Grafana with AWS services often requires more manual setup (e.g., provisioning IAM roles, configuring data sources) than the managed integration available with QuickSight, making it higher overhead in a purely AWS-native context. Option B also suffers from the same Redshift-overkill logic if interpreted as an alternative, but specifically pairs Glue with DynamoDB, which is less standard for JDBC CDC than DMS.

Community Comment Notes

Many users voted for A, arguing that DynamoDB is not a relational database and thus unsuitable for JDBC sources. However, DMS handles the translation from relational to NoSQL seamlessly. One user noted that 'DynamoDB is not designed to support relational databases,' missing the point that DMS performs the migration/transformation. Another user correctly identified that DMS + DynamoDB + QuickSight offers the least dev overhead, highlighting that building ETL jobs (Glue) is more labor-intensive than configuring DMS tasks.

Exam Strategy

Focus on the constraint 'LEAST development overhead.' When asked for minimal coding/customization, prefer managed services that handle data movement automatically (like DMS CDC) over services that require pipeline construction (like Glue). Also, consider the visualization tool's native integration capabilities.

Frequently Asked Questions

Why is AWS DMS better than AWS Glue here?

DMS provides out-of-the-box Change Data Capture (CDC) for JDBC sources, requiring zero code for ingestion. Glue requires writing and managing Spark/Python ETL scripts.

Can QuickSight visualize DynamoDB data?

Yes, QuickSight can connect directly to DynamoDB via Amazon Athena or its native connector, allowing for quick dashboard creation without intermediate storage.

More DEA-C01 FAQ →

Community Votes

A
70%
D
30%

70% of anonymous learners picked answer A. Votes are pick records left by other test-takers — they are not the verified answer.

Discussion 5 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 5

  1. pepedaruiz999 👍 3 Answered A
    DynamoDB is not relational data base
  2. kailu 👍 2 Answered D
    Using AWS DMS for real-time change data capture (CDC) and publishing the changes to DynamoDB, followed by building QuickSight dashboards, is the most efficient solution with the least development overhead for this use case
  3. emupsx1 👍 2 Answered A
    https://docs.aws.amazon.com/prescriptive-guidance/latest/patterns/build-an-etl-service-pipeline-to-load-data-incrementally-from-amazon-s3-to-amazon-redshift-using-aws-glue.html
  4. MerryLew 👍 2 Answered A
    DynamoDB is not designed to support relational databases. Redshift, however is.
  5. axantroff 👍 1 Answered D
    IDK, it feels like from DEV overhead D > A

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