Describe considerations for working with non-relational data on Azure →mediumMultiple SelectObjective-mapped
DP-900 Practice Question: Describe considerations for working with non-relational data on Azure
A company is designing a solution to store time-series data from millions of IoT devices. Which TWO Azure services are most suitable for this scenario?
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 (option A) is optimized for time-series analytics and ingesting high volumes of data from IoT devices. Azure Cosmos DB (option C) provides a flexible schema and low latency suitable for time-series data storage. Azure Blob Storage (option B) is for unstructured blob data, not optimized for time-series queries. Azure Redis Cache (option D) is a caching layer, not a primary storage solution. Azure SQL Database (option E) is relational and less efficient for high-velocity time-series data.
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 purpose-built for storing and analyzing time-series and high-throughput telemetry data. Its columnar storage and specialized Kusto Query Language (KQL) engine index data by time partitions, enabling fast, server-side aggregations and native time-series functions like make-series and anomaly detection. This makes it the most appropriate choice for interactive analytics over large volumes of timestamped events.
- ✗
Azure Blob Storage
Why it's wrong here
Azure Blob Storage is an object store designed for unstructured data such as files, images, and backups, not for querying time-series records. It lacks native indexing, time-based partitioning, or a query engine capable of slicing data by time ranges at interactive speed, so any time-series analysis would require exporting data to a separate platform like Azure Data Explorer or a data warehouse. Its append-only nature can store raw logs, but it is not a direct queryable time-series solution.
- ✓
Azure Cosmos DB
Why this is correct
Azure Cosmos DB is a fully managed NoSQL database that supports flexible schemas and single-digit-millisecond writes, making it viable for capturing high-frequency time-series data such as IoT telemetry. Its partitioning by device or sensor ID can scale ingestion, and queries can filter on a timestamp field, but it lacks the specialized time-series analytics primitives and compression of a columnar store. For simple retrieval or write-heavy workloads, it is an acceptable correct choice, though not as efficient as Azure Data Explorer for complex analytical queries.
- ✗
Azure Redis Cache
Why it's wrong here
Azure Redis Cache is an in-memory key-value store primarily used for caching and session state, not for persistent time-series storage. Although Redis supports sorted sets and time-series modules, it holds data in RAM, which is costly and limited in capacity, and unless persistence is explicitly configured, data is lost on restart. It cannot handle the volume and durability requirements of a long-term time-series solution, and query capabilities are far less analytical than dedicated time-series engines.
- ✗
Azure SQL Database
Why it's wrong here
Azure SQL Database is a row-based relational database with transaction overhead and indexing requirements that make it inefficient for high-ingestion, append-only time-series workloads. While you can store timestamps and use clustered indexes, every insert incurs row and index maintenance, and analytical queries over large time ranges can suffer from high I/O and lock contention. It is better suited for operational data with relationships than for the high-volume, low-latency writes and time-window analytics typical of time-series data.
Quick reference
Azure Blob Storage Tier Comparison
| Tier | Storage Cost | Retrieval Cost | Latency | Use Case |
|---|---|---|---|---|
| Hot | Highest | Lowest | Immediate | Active data, frequent reads |
| Cool | Lower | Higher | Immediate | Data accessed < once / month |
| Cold | Lower still | Higher | Immediate | Data accessed < once / quarter |
| Archive | Lowest | Highest + rehydration delay | Hours | Long-term compliance retention |
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Schema
A schema is a blueprint or logical structure that defines how data is organized, stored, and accessed in a database or information system.
Key term
Redis
Redis is an open-source, in-memory data structure store used as a database, cache, and message broker for high-speed data access.
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