DP-203 Develop data processing Practice Question
You are designing a data processing solution in Azure Synapse Analytics. The solution must process streaming data from Azure Event Hubs and store the results in a dedicated SQL pool. You need to choose the most appropriate service for near real-time ingestion with minimal latency. What should you use?
⚠ Common exam trap
Many candidates confuse 'near real-time' with 'batch processing' and choose Azure Data Factory (option C) because it is a familiar data integration tool, overlooking that it lacks native streaming capabilities and introduces latency from scheduled pipeline runs.
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 purpose-built for near real-time stream processing with sub-second latency, directly integrates with Azure Event Hubs as an input source and dedicated SQL pool as an output sink, and provides a SQL-like query language for defining transformations. This minimizes architectural complexity and latency compared to other services.
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 Databricks with Structured Streaming
Why it's wrong here
Structured Streaming runs on Spark clusters, adding job-start and micro-batch scheduling latency that a dedicated SQL pool sink cannot absorb for near real-time writes. It suits complex stateful transformations over Event Hubs streams, not minimal-latency ingestion into a dedicated SQL pool.
- ✓
Azure Stream Analytics
Why this is correct
Azure Stream Analytics is a fully managed, serverless stream-processing engine that ingests directly from Event Hubs and writes to a dedicated SQL pool, delivering sub-second near real-time latency without provisioning clusters. This satisfies the stem's minimal-latency ingestion constraint, unlike batch-oriented alternatives such as Synapse pipelines or Spark structured streaming jobs.
- ✗
Azure Data Factory
Why it's wrong here
Data Factory uses time-sliced pipeline triggers with per-activity orchestration overhead, so it cannot sustain the continuous low-latency flow Event Hubs requires into a dedicated SQL pool. It is the right choice for scheduled batch movement and orchestration between stores, not sub-second streaming ingestion.
- ✗
Azure Functions with Event Hub trigger
Why it's wrong here
Azure Functions with an Event Hub trigger scales per-event and suits lightweight, event-driven processing, but its cold-start and per-invocation overheads add latency and it cannot write directly into a dedicated SQL pool at sustained streaming throughput. Stream Analytics or Spark Structured Streaming is the near real-time ingestion path.
Go deeper
Related to this question
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Implement Azure Stream Analytics
Key term
Azure Stream Analytics
Azure Stream Analytics is a fully managed, real-time data processing service that analyzes and transforms high volumes of streaming data from various sources to deliver low-latency insights and trigger actions.
Key term
Azure Synapse Analytics
Azure Synapse Analytics is a cloud-based data integration, warehousing, and analytics service that brings together big data and data warehouse capabilities under one platform.
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