DP-900 Describe an analytics workload on Azure Practice Question
A data engineering team needs to build a real-time dashboard showing sales totals by region. Sales transactions are streamed from point-of-sale systems into Azure Event Hubs. The team wants to aggregate the data in near real-time (e.g., every minute) and store the results in Azure SQL Database for visualization in Power BI. Which Azure service should they use for the aggregation step?
⚠ Common exam trap
Watch out — candidates often confuse Azure Data Factory or Synapse Pipelines (which are batch-oriented) with real-time processing, overlooking that Stream Analytics is the only service among the options purpose-built for continuous, low-latency stream aggregation.
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 Stream Analytics
Azure Stream Analytics is the correct choice because it is designed for real-time stream processing, allowing you to define a query that aggregates sales data from Event Hubs over a one-minute tumbling window and output the results directly to Azure SQL Database. This meets the requirement for near real-time aggregation without needing to write custom code or manage infrastructure.
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 this is correct
Azure Stream Analytics is a fully managed real-time stream processing engine within Azure, designed to run continuous, low-latency SQL-like queries over data from sources such as Event Hubs and IoT Hub. It supports windowed aggregations (tumbling, hopping, sliding, session) and can output results directly to Azure SQL Database or Power BI, making it the appropriate service for a real-time dashboard. Unlike the other options, its entire runtime is optimized for streaming data rather than batch movement or workflow integration.
- ✗
Azure Data Factory
Why it's wrong here
Azure Data Factory is a cloud-based ETL and ELT service that orchestrates and automates data movement at scheduled intervals or via event-driven triggers, but it processes data in batches, not as a continuous stream. It cannot perform windowed aggregations or real-time analytics; instead, it copies and transforms data between stores using pipelines that run on a schedule. While it could move a batch of dashboard data periodically, it does not deliver the sub-second, ongoing processing required for a real-time dashboard.
- ✗
Azure Synapse Pipelines
Why it's wrong here
Azure Synapse Pipelines, the data integration orchestration engine inside Synapse Analytics, handles batch data movement and transformation workflows similar to Data Factory, but they are not built for real-time stream processing. They lack native support for continuous queries, windowing operations, or direct ingestion from streaming sources with low-latency output. Synapse offers other components like Spark Streaming or dedicated SQL pools for analytics, but the Pipelines service itself is designed for scheduled, batch-oriented data loading rather than powering a live dashboard.
- ✗
Azure Logic Apps
Why it's wrong here
Azure Logic Apps is a low-code integration and workflow service that connects applications and services through hundreds of connectors, enabling automation of business processes and event-driven workflows. It is not a data processing engine and does not perform aggregation, windowing, or statistical analysis on streaming data; its role is to orchestrate HTTP calls, messages, and files. Using Logic Apps to build a real-time dashboard would require offloading the actual computation to other services, as it cannot query or summarize a live stream.
Go deeper
Related to this question
Learn chapter
Data Roles and Core Concepts
Key term
Stream processing
Stream processing is a data processing method that continuously analyzes and acts on data in real time as it arrives, rather than storing it first and processing it later.
Key term
Data
Data is raw, unprocessed information, like numbers, words, or measurements, that can be stored, processed, and analyzed by computers.
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