A utility company collects several million meter readings a day from smart devices. Analysts query the data each morning, and anything older than a month should move to a cheaper class of storage. Which platform is the best fit?
Show answer and explanation
Answer: C
Data Lake Storage Gen2 combines inexpensive blob capacity with a hierarchical namespace that analytics engines can read efficiently. Lifecycle management rules can move older files to cooler or archive tiers on a schedule. SQL Database costs more per gigabyte at this scale and is tuned for transactional workloads. A Cosmos DB time-to-live removes records rather than archiving them, which discards history the company wants to keep. Azure Files is a file share, not an analytics store.
Why the other options are wrong
- A. Elastic pools share compute across transactional databases. Storing millions of readings a day at this scale is costly compared with object storage built for analytics.
- B. A time-to-live deletes items after expiry instead of moving them to a cheaper tier, so historical readings would be gone and could not be analyzed later.
- D. Premium file shares charge for performance and serve SMB or NFS clients. They offer no tiering rules and no analytics-friendly layout for this workload.
Exam tip. For analytics data with retention rules, Data Lake Storage Gen2 with lifecycle policies is the usual pattern.