DP-203 Design and implement data storage Practice Question
Your team needs to provide near-real-time analytics on IoT sensor data streaming into Azure Event Hubs. The data must be stored in Azure Data Lake Storage Gen2 in Parquet format, partitioned by date and device ID. Which architecture should you implement?
⚠ Common exam trap
Microsoft often tests the misconception that any service capable of reading from Event Hubs is suitable for near-real-time analytics, ignoring the critical requirements for native partitioning, low latency, and managed checkpointing that Stream Analytics uniquely provides.
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
✓
Use Azure Stream Analytics with output to Data Lake Storage Gen2, using partitioning by date and device ID.
Azure Stream Analytics provides native, low-latency processing of streaming data from Event Hubs with direct output to Azure Data Lake Storage Gen2. It supports automatic partitioning by specifying a partition key (e.g., date and device ID) in the output configuration, enabling efficient, near-real-time writes in Parquet format without additional orchestration.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use Azure Stream Analytics with output to Data Lake Storage Gen2, using partitioning by date and device ID.
Why this is correct
Stream Analytics provides native partitioning and Parquet output.
- ✗
Use Azure Data Factory with a tumbling window trigger to copy data from Event Hubs to Data Lake Storage.
Why it's wrong here
ADF is not designed for real-time streaming from Event Hubs.
- ✗
Use Azure Functions to read from Event Hubs and write to Data Lake Storage.
Why it's wrong here
Functions have scaling limitations and require manual partitioning logic.
- ✗
Use Azure Databricks with Structured Streaming to read from Event Hubs and write to Data Lake Storage.
Why it's wrong here
Azure Databricks with Structured Streaming can process data from Event Hubs and write to Data Lake Storage. However, for near-real-time ingestion and simple partitioning of raw data, it introduces significant operational overhead and cost due to managing Spark clusters. This option is tempting because Databricks excels at complex stream processing, real-time transformations, and machine learning model inference on streaming data, making it ideal for scenarios requiring advanced analytics beyond basic ingestion and storage.
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