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Difficulty: Very hardAWS Database Services

A utility company is architecting a smart grid telemetry system on AWS. The system must ingest high-frequency, key-value power consumption readings from millions of smart meters globally with single-digit millisecond latency. Separately, the business intelligence team needs to perform complex, multi-year analytical queries on this historical data to forecast regional energy demand. To achieve a loosely coupled architecture, the company wants to select the appropriate database services.

Which combination of AWS database services should the company choose to meet these requirements? (Select TWO.)

  1. Amazon DynamoDB to ingest and store the high-frequency key-value telemetry dataAnswer
  2. Amazon Redshift to act as a data warehouse for complex analytical queries on historical dataAnswer
  3. C
    Amazon Aurora to consolidate all workloads, handling both the high-frequency writes and the multi-year analytical queries within a single database cluster
  4. D
    Amazon DynamoDB to store the historical archive and run complex SQL join queries across multiple dimensions
  5. E
    A database hosted on Amazon EC2 to automatically transfer database operating system patching and scaling responsibilities to AWS

Answer

The correct database combination consists of Amazon DynamoDB for telemetry ingestion and Amazon Redshift for multi-year data warehousing and analytical queries.
The correct response involves choosing the option describing Amazon DynamoDB to ingest and store the high-frequency key-value telemetry data, alongside the option describing Amazon Redshift to act as a data warehouse for complex analytical queries. Amazon DynamoDB is a NoSQL service built for low-latency, high-volume key-value ingestion, which aligns with the smart meter write requirements. Amazon Redshift is an OLAP (Online Analytical Processing) data warehouse optimized for complex queries across massive historical datasets. Together, they create a specialized, decoupled architecture.

Step-by-Step Solution

1
Analyze the data ingestion and write-latency requirement.
The utility system requires high-frequency key-value writes from millions of smart meters with single-digit millisecond latency.
This workload demands a highly scalable NoSQL database optimized for rapid key-value operations, pointing to Amazon DynamoDB.
2
Analyze the query and analytical analysis requirement.
The business intelligence team needs to run complex, long-running analytical queries across years of historical data.
This workload requires a columnar database optimized for data warehousing and large-scale SQL queries, pointing to Amazon Redshift.
3
Evaluate the architectural design against loose coupling principles.
Combining transactional ingestion and analytical forecasting into a single relational database cluster is rejected.
Using separate specialized database services (DynamoDB for transactional writes, Redshift for analytical reads) prevents performance degradation and supports independent scaling.
4
Evaluate administrative responsibility constraints.
Self-managed database hosting on Amazon EC2 is rejected due to customer overhead.
Hosting a database on Amazon EC2 does not offload OS patching or scaling to AWS, whereas fully managed services (DynamoDB and Redshift) do.

Key Concept

Selecting specialized, decoupled database services based on transactional versus analytical requirements
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AWS Database Services

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