AZ-305 Design data storage solutions Practice Question
A company needs to store and analyze petabytes of semi-structured data from IoT devices. The data is append-only and written in time order. They need to support fast queries on time ranges and also aggregate data in real-time. Which Azure data service should they use?
⚠ Common exam trap
Candidates often confuse Azure Data Explorer with Azure Cosmos DB because both handle semi-structured data, but Cosmos DB is optimized for transactional workloads with point reads and writes, not for petabyte-scale analytical time-series queries.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Azure Data Explorer
Azure Data Explorer (ADX) is purpose-built for high-performance analysis of large volumes of time-series and semi-structured data. It supports append-only ingestion, optimized time-range queries via its columnar storage and indexing, and real-time aggregation using Kusto Query Language (KQL) with built-in materialized views and update policies.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Azure Data Explorer
Why this is correct
Azure Data Explorer is a purpose-built analytics engine for petabyte-scale, time-series data that arrives continuously from IoT sources. It ingests semi-structured payloads (JSON, Avro, etc.) without requiring a predefined schema, then applies columnar storage and a distributed sharding architecture that accelerates real-time aggregation and time-window queries. Its Kusto Query Language (KQL) is designed for slicing, rolling averages, and anomaly detection on streaming telemetry, which makes it the correct choice here.
- ✗
Azure Cosmos DB
Why it's wrong here
Azure Cosmos DB is a multi-model NoSQL database engineered for globally distributed, low-millisecond transactional access to documents, graphs, and key-value pairs, not for run-of-the-mill analytical workloads. Although it accepts semi-structured documents, its per-request consumption model and throughput provisioning make petabyte-scale append-only IoT analytics expensive and slow, because every record insert consumes RU/s and cross-partition aggregation queries are costly. It also lacks native time-window partitioning and real-time aggregation capabilities, so it would need external pipelines to approximate ADX's behavior.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a relational engine that mandates a fixed schema upfront, which conflicts with the highly variable, semi-structured telemetry produced by diverse IoT devices. It is optimized for OLTP transactions and requires careful index tuning to avoid write amplification; at petabytes of append-only data, this becomes unmanageable and violates the fast ingest pattern of most IoT workloads. Time-series analysis in T-SQL relies on traditional window functions and requires periodic table maintenance, but does not offer the native columnar time-partitioned storage or distributed query engine needed for real-time analysis.
- ✗
Azure Table Storage
Why it's wrong here
Azure Table Storage is a simple, NoSQL key-value store where every row is a (partition key, row key) pair that supports fast point lookups, but not scanning, complex filters, or aggregation queries. It can scale to store petabytes of structured data, but because it has no secondary indexes or time-series-specific partitioning, running real-time analytic expressions like count, average, or percentile over a feeding IoT dataset would require client-side paging and full scans. Its narrow data model also treats every property as opaque strings/bytes, making it poor for semi-structured telemetry that changes frequently.
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Senior Network & Security Engineer · founder of Courseiva
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