Topic 1 Question 234

Professional Data Engineer

Topic 1 Question 234
You migrated a data backend for an application that serves 10 PB of historical product data for analytics. Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?
- 1. Store the historical data in BigQuery for analytics.
  2. Use a materialized view to precompute the last state of a product.
  3. Serve the last state data directly from BigQuery to the API.
- 1. Store the products as a collection in Firestore with each product having a set of historical changes.
  2. Use simple and compound queries for analytics.
  3. Serve the last state data directly from Firestore to the API.
- 1. Store the historical data in Cloud SQL for analytics.
  2. In a separate table, store the last state of the product after every product change.
  3. Serve the last state data directly from Cloud SQL to the API.
- 1. Store the historical data in BigQuery for analytics.
  2. In a Cloud SQL table, store the last state of the product after every product change.
  3. Serve the last state data directly from Cloud SQL to the API.
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- 正解だと思う選択肢: A
  A. 1. Store the historical data in BigQuery for analytics. 2. Use a materialized view to precompute the last state of a product. 3. Serve the last state data directly from BigQuery to the API.
  
  👍 2
  scaenruy2024/01/03
- Serve the last state data directly from Cloud SQL to the API. Here's why this option is most suitable:
  
  BigQuery for Analytics: BigQuery is an excellent choice for storing and analyzing large datasets like your 10 PB of historical product data. It is designed for handling big data analytics efficiently and cost-effectively.
  
  Cloud SQL for Last State Data: Cloud SQL is a fully managed relational database that can effectively handle the storage of the last known state of products. Storing this subset of data (about 10 GB) in Cloud SQL allows for optimized and faster query performance for your API needs. Cloud SQL can comfortably handle the requirement of up to 1000 QPS with sub-second latency.
  
  Separation of Concerns: This approach separates the analytics workload (BigQuery) from the operational query workload (Cloud SQL). This separation ensures that analytics queries do not interfere with the operational performance of the API and vice versa.
  
  👍 2
  einchkrein2024/01/08
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