DP-203 Develop data processing Practice Question
You are designing a data processing solution in Azure Databricks to transform streaming data from Azure Event Hubs. The data must be aggregated in 1-minute tumbling windows and written to Azure Synapse Analytics. Which Spark API should you use?
⚠ Common exam trap
It's easy for candidates to confuse the older Spark Streaming (DStreams) API with Structured Streaming, assuming both are equally capable for event-time windows, but DStreams lack native event-time support and are deprecated in favor of Structured Streaming.
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
✓
Structured Streaming
Structured Streaming is the correct choice because it provides native support for event-time-based aggregations, such as 1-minute tumbling windows, and integrates seamlessly with Azure Event Hubs as a streaming source and Azure Synapse Analytics as a streaming sink using the `foreachBatch` or `writeStream` API. It offers exactly-once semantics and automatic state management for windowed operations, which are essential for reliable streaming ETL.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
RDD API
Why it's wrong here
The RDD API exposes no schema, Catalyst optimiser, or built-in event-time windowing, so 1-minute tumbling aggregations must be hand-coded and cannot handle late-arriving Event Hubs data. RDDs suit low-level unstructured transformations, not structured streaming writes to Synapse.
- ✓
Structured Streaming
Why this is correct
Structured Streaming satisfies the 1-minute tumbling window requirement through its event-time windowing on the streaming DataFrame, aggregating Event Hubs data incrementally. It writes results to Azure Synapse Analytics via the Synapse connector, unlike DStreams (RDD-based, deprecated) or batch APIs, which cannot process continuous streams natively.
- ✗
Spark Streaming (DStreams)
Why it's wrong here
DStreams use micro-batch RDDs with processing-time semantics and no native event-time watermarking, so late Event Hubs events break 1-minute tumbling windows. DStreams suit legacy Spark 1.x pipelines; Structured Streaming provides the event-time windowing and Synapse connector this scenario requires.
- ✗
DataFrame API with batch processing
Why it's wrong here
Batch DataFrames read bounded data, so they cannot consume a continuous Event Hubs stream or maintain 1-minute tumbling window state. Batch processing suits scheduled transformations over data already landed in storage, not continuous ingestion requiring Structured Streaming's watermarking and windowed aggregation.
Go deeper
Related to this question
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Implement Azure Synapse Analytics
Key term
Azure Databricks
Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform optimized for Azure that lets data teams prepare data, run machine learning models, and build data pipelines using a single workspace.
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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