AZ-305 Design data storage solutions Practice Question
A company stores petabytes of sensor data in Azure Data Lake Storage Gen2. They need to run complex analytics queries that involve joining multiple datasets and aggregating time-series data. The queries must complete within seconds. Which Azure service should they use for querying?
⚠ Common exam trap
Watch out — candidates often confuse Azure Data Explorer (Option B) as the best choice for time-series data, but the question specifies complex joins across multiple datasets and direct querying of Data Lake Storage Gen2, which Synapse handles natively while Data Explorer requires data ingestion and is not designed for multi-table joins at petabyte scale.
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 Synapse Analytics
Azure Synapse Analytics (Option C) is correct because it provides a unified analytics platform that can directly query petabyte-scale data in Azure Data Lake Storage Gen2 using T-SQL or Spark, and its distributed query engine (PolyBase or Synapse SQL) can perform complex joins and time-series aggregations with sub-second response times when combined with appropriate indexing and materialized views.
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 Stream Analytics
Why it's wrong here
Azure Stream Analytics is a serverless real-time event-processing engine designed for continuous, low-latency queries over streaming sources like IoT telemetry, not for interactive batch analytics over historical petabytes. Its SQL-like job model operates over tumbling/sliding windows on incoming events, and it does not persist or index large static datasets for ad-hoc exploration. Attempting to use it against a data lake would require replaying stored files as a stream, which is inefficient and sacrifices the snapshot isolation and dynamic partitioning needed for responsive interactive queries.
- ✗
Azure Data Explorer
Why it's wrong here
Azure Data Explorer (ADX) is a fast time-series and log analytics engine that excels at high-velocity ingestion and simple filter/aggregation queries on telemetry data, but it is not architected for complex relational joins and star-schema SQL workloads across petabyte-scale data lake files. Its query engine relies on pre-ingested indexed shards in its own storage, and while it can read from data lake storage, doing so for huge external tables loses its core compression and indexing advantages. For interactive queries over sensor data requiring multi-table joins and reporting, a massively parallel relational engine is far more appropriate.
- ✓
Azure Synapse Analytics
Why this is correct
Azure Synapse Analytics is the correct choice because it combines a massively parallel processing (MPP) SQL engine with direct query access to data stored in Azure Data Lake Storage through both dedicated SQL pools and serverless SQL, enabling fast interactive T-SQL queries over petabyte volumes. Its dedicated pool distributes rows across 60 compute nodes with columnstore indexes, while serverless provides per-query billing without provisioning. This architecture supports complex joins, aggregations, and BI reporting at the scale and concurrency expected from a data warehouse, making it the best fit for the sensor data lake.
- ✗
Azure Databricks
Why it's wrong here
Azure Databricks is a powerful Apache Spark-based analytics platform for large-scale data engineering, ETL, and machine learning, but for low-latency interactive SQL workloads on a data lake it is slower than Synapse's MPP engine. Spark's JVM-based scheduling and row-oriented execution introduce higher per-query overhead than Synapse's compiled, columnstore-optimized relational engine, often requiring cluster warm-up and yielding weaker concurrency for simultaneous dashboard queries. It is better suited for batch transformations or ML pipelines that tolerate longer runtimes, not for ad-hoc interactive analysis.
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