You mentioned storing data in ATL. Can you explain what ATL is and why you chose it?

ATL most commonly refers to Azure Table Storage, a NoSQL key‑value store that is part of Azure Storage. It stores entities in tables, where each entity is a collection of properties identified by a partition key and a row key. The service is highly scalable, offers low latency, and is cost‑effective for large volumes of semi‑structured data. It is ideal for scenarios where you need to store simple, schema‑less data, such as device telemetry, logs, or user profiles, and you don’t require complex joins or relational constraints. Because the data model is flat, you can quickly ingest millions of rows without the overhead of a relational database. When you need richer querying or analytics, you can move the data to Azure SQL, Cosmos DB, or a data warehouse like Synapse or Snowflake. In the interview context, the candidate likely chose ATL to store raw device data because it provides fast ingestion, easy scaling, and low cost, while the downstream pipeline can transform and load the data into a structured store for analytics.