Real-Time and Batch Analytics for Streaming Data
You are designing a data storage solution for a real-time dashboard that displays streaming data from Azure Event Hubs. The data must be stored in a format that supports both real-time and batch analytics with minimal latency. Which TWO technologies should you use?
Quick Answer
The answer is Azure Stream Analytics and Azure Synapse Analytics. Azure Stream Analytics is correct because it provides real-time stream processing with minimal latency, directly ingesting data from Event Hubs and enabling real-time dashboard queries while simultaneously writing results to a staging store like Azure Data Lake Storage. Azure Synapse Analytics then supports batch analytics by querying that stored historical data, creating a unified solution for both real-time and batch analytics from streaming data. On the DP-203 exam, this scenario tests your understanding of the Lambda architecture pattern, where a speed layer (Stream Analytics) handles live data and a batch layer (Synapse) handles historical analysis. A common trap is choosing Azure Databricks for the real-time path, but Stream Analytics is the native, low-latency service for Event Hubs ingestion. Memory tip: think “Stream for speed, Synapse for deep analysis” to pair the two correctly.
⚠ Common exam trap
Many exam-takers confuse Azure Data Factory as a real-time processing tool, but it is strictly a batch orchestration service with no native stream processing capability.
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 correct because it provides real-time stream processing with low latency, directly ingesting data from Event Hubs and outputting to storage or analytics services. It enables both real-time dashboard queries and batch analytics by writing to a staging store like Azure Data Lake Storage, which can then be queried by Azure Synapse Analytics for historical analysis.
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
Stream Analytics processes streaming data in real-time.
- ✗
Azure Data Factory
Why it's wrong here
Primarily for batch data movement, not real-time.
- ✓
Azure Synapse Analytics
Why this is correct
Supports both real-time (via Synapse Link) and batch analytics.
- ✗
Azure Analysis Services
Why it's wrong here
Used for semantic models, not data storage.
- ✗
Azure SQL Database
Why it's wrong here
Not optimized for real-time streaming ingestion.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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.
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.
About these practice questions
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Same concept, more angles
1 more way this is tested on DP-203
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. You need to design a storage solution for streaming data from IoT devices. The solution must support real-time analytics and long-term storage for historical analysis. Which combination of Azure services should you use?
easy- A.Azure Queue Storage and Azure Cosmos DB
- B.Azure Event Hubs and Azure Blob Storage
- C.Azure IoT Hub and Azure SQL Database
- ✓ D.Azure Event Hubs and Azure Data Lake Storage Gen2
Why D: Azure Event Hubs is designed for high-throughput, low-latency ingestion of streaming data from IoT devices, supporting real-time analytics via integration with Azure Stream Analytics. Azure Data Lake Storage Gen2 provides hierarchical namespace and POSIX-compliant access for long-term storage, enabling efficient historical analysis with tools like Azure Synapse Analytics or Spark. This combination meets both real-time and historical requirements without the limitations of other options.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This DP-203 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the DP-203 exam.