Question

Difficulty: MediumAWS Database Services

A gaming company is launching a new multiplayer game. They require a fully managed database solution to store real-time player profiles and session state data, which has a highly flexible schema and demands sub-millisecond, single-digit latency response times. Additionally, their data analyst team needs to store and analyze millions of rows of historical game logs from the past year to run complex, long-running business intelligence queries. Which TWO of the following solutions should the company implement to meet these requirements?

  1. Amazon DynamoDB to store player profiles and session state dataAnswer
  2. Amazon Redshift to run complex analytical queries on historical game logsAnswer
  3. C
    Amazon RDS for MySQL to store player profiles and session state data
  4. D
    Amazon Aurora configured as a single database to handle both operational session states and historical analytics
  5. E
    A self-managed PostgreSQL database hosted on Amazon EC2 to minimize administrative overhead for backups and OS patching

Answer

The company should implement Amazon DynamoDB to store player profiles and session state data, and Amazon Redshift to run complex analytical queries on historical game logs.
The correct services are Amazon DynamoDB and Amazon Redshift. Amazon DynamoDB provides the required single-digit millisecond latency and flexible key-value schema needed for live player profile and session state tracking. Amazon Redshift is designed specifically as a data warehouse to handle complex, long-running analytical queries on massive amounts of historical data without impacting the operational database.

Step-by-Step Solution

1
Analyze the operational workload requirements.
The application needs sub-millisecond, low-latency access to flexible-schema player profiles and session states.
This points to a NoSQL database solution designed for high scale and key-value lookups, such as Amazon DynamoDB.
2
Analyze the analytical workload requirements.
The business intelligence team needs to perform complex, long-running queries over massive volumes of historical log data.
This is an Online Analytical Processing (OLAP) use case, which is best addressed by a dedicated data warehouse solution like Amazon Redshift.
3
Evaluate the wrong options.
Using relational options for NoSQL workloads, consolidating transactional and analytical workloads, or self-managing databases on EC2 fail to meet performance, design, or management overhead goals.
Decoupling workloads and leveraging the appropriate managed service types aligns with AWS architectural best practices.

Key Concept

Selecting the correct AWS database service based on workload characteristics (NoSQL for low-latency operational data vs. data warehousing for complex analytics) and understanding the managed service responsibility model.
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