Data Store Management III
Explore practical solutions for managing data stores in AWS to improve query efficiency, support schema evolution, enable AI-driven document retrieval, optimize Redshift loading, implement secure column-level access, and manage Kafka storage cost-effectively. This lesson enhances your ability to optimize and secure AWS data platforms.
We'll cover the following...
Question 34
A company has an Amazon DynamoDB table for an e-commerce product catalog. The table uses product_id as the partition key. Analysts frequently need to query products by category and sort results by price. The current table design requires full table scans for these queries, resulting in high latency and read capacity consumption.
Which design change should the data engineer implement to support this access pattern efficiently?
A. Create a Global Secondary Index (GSI) with category as the partition key and price as the sort key.
B. Create a Local Secondary Index (LSI) with price as the sort key.
C. Add a scan filter on category and price to the existing table queries.
D. Create a separate DynamoDB table for each product category.
Question 35
A data lake team uses Apache Iceberg tables on Amazon S3 with the AWS Glue Data Catalog as the Iceberg catalog. A new business requirement mandates adding a non-nullable column to an existing Iceberg table that contains terabytes of data. The team needs to evolve the schema without rewriting existing data files. ...