AWS Certified Associate Data-Engineer-Associate Passing Score

The requirement is to manage the size of an Amazon DynamoDB table by automatically deleting data older than 1 month without disrupting ongoing read or write operations. The simplest and most maintenance-free solution is to useDynamoDB Time-to-Live (TTL).

Option A: Use the DynamoDB TTL feature to automatically expire data based on timestamps.DynamoDB TTL allows you to specify an attribute (e.g., a timestamp) that defines when items in the table should expire. After the expiration time, DynamoDB automatically deletes the items, freeing up storage space and keeping the table size under control without manual intervention or disruptions to ongoing operations.

Other options involve higher maintenance and manual scheduling or scanning operations, which increase complexity unnecessarily compared to the native TTL feature.

According to the guide:

“For integrating streaming data from Amazon MSK into Amazon Redshift efficiently and cost-effectively, AWS Glue streaming jobs can process and transform the data, storing it in Amazon S3. Amazon Redshift Spectrum can then directly query the data from S3, minimizing operational overhead and reducing storage costs.”

–Ace the AWS Certified Data Engineer - Associate Certification - version 2 - apple.pdf

This setup offers:

Low latencyvia Glue streaming.

Low storage costby using Parquet/ORC on S3.

Minimal operational overheadby avoiding complex pipelines or constantly updated materialized views.

In Amazon Redshift, theSUPERdata type is designed specifically to handle semi-structured data like JSON, Parquet, ORC, and others. By using the SUPER data type, Redshift can ingest and query JSON data without requiring complex data flattening processes, thus reducing the amount of preprocessing required before loading the data. TheSUPERdata type also works seamlessly withRedshift Spectrum, enabling complex queries that can combine both structured and semi-structured datasets, which aligns with the company’s need to use combined datasets to identify potential new customers.

Using the SUPER data type also allows forautomatic parsing and query processingof nested data structures through Amazon Redshift'sPARTITION BYandJSONPATH expressions, which makes this option the most efficient approach with the least effort involved. This reduces the overhead associated with using tools like AWS Glue or Lambda for data transformation.

Problem Analysis:

The company requires acentralized data warehousefor consolidating data from various sources.

They useAmazon QuickSight in direct query mode, necessitatingfast response timesfor analytical queries.

Users query the data intermittently, withunpredictable spikesduring the day.

Operational overhead should be minimal.

Key Considerations:

The solution must support fast, SQL-based analytics.

It must handle unpredictable spikes efficiently.

Must integrate seamlessly withQuickSightfor direct querying.

Minimize operational complexity and scaling concerns.

Solution Analysis:

Option A: Amazon Redshift Serverless

Redshift Serverless eliminates the need for provisioning and managing clusters.

Automatically scales compute capacity up or down based on query demand.

Reduces operational overhead by handling performance optimization.

Fully integrates withAmazon QuickSight, ensuring low-latency analytics.

Reduces costs as it charges only for usage, making it ideal for workloads with intermittent spikes.

Option B: Amazon Athena with S3 (Apache Parquet)

Athena supports querying data directly from S3 in Parquet format.

While it’s cost-effective, performance depends on the size and complexity of the data.

It is not optimized for high-speed analytics needed by QuickSight in direct query mode.

Option C: Amazon Redshift Provisioned Clusters

Requires manual cluster provisioning, scaling, and maintenance.

Higher operational overhead compared to Redshift Serverless.

Option D: Amazon Aurora PostgreSQL

Aurora is optimized for transactional databases, not data warehousing or analytics.

Does not meet the requirement for fast analytics queries.

Final Recommendation:

Amazon Redshift Serverlessis the best choice for this use case because it provides fast analytics, integrates natively with QuickSight, and minimizes operational complexity while efficiently handling unpredictable spikes.