DP-203 Structured Streaming Practice Question
You are using Azure Synapse Analytics to process streaming data from Azure Event Hubs. The data must be written to a Delta Lake table in ADLS Gen2 with exactly-once semantics. Which processing engine should you use?
⚠ Common exam trap
Candidates often assume that Azure Synapse Pipeline with Mapping Data Flow is suitable for real-time streaming because it can handle incremental data loads, but it is actually a batch transformation tool. The correct streaming engine for exactly-once semantics with Delta Lake is Azure Databricks 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
✓
Azure Databricks with Structured Streaming
Azure Databricks with Structured Streaming is the correct choice because it natively supports exactly-once semantics when writing to Delta Lake from Event Hubs. Structured Streaming uses checkpointing and a write-ahead log to ensure each record is processed exactly once, even in the face of failures. Azure Databricks runs on Spark, which integrates seamlessly with both Event Hubs (via the Event Hubs connector) and Delta Lake (as a sink). Other options are either batch-oriented or lack the necessary transactional guarantees for exactly-once delivery to Delta Lake.
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 this is correct
Azure Databricks Structured Streaming natively supports Delta Lake sinks with idempotent writes and checkpointing, delivering exactly-once semantics when consuming Event Hubs. This satisfies the stem's exactly-once requirement, which plain Spark or Synapse streaming cannot guarantee without additional transactional handling.
- ✗
Azure Synapse serverless SQL pool
Why it's wrong here
The serverless SQL pool only queries data with T-SQL; it cannot ingest Event Hubs streams or write Delta tables, so no checkpointing or exactly-once guarantee exists. It is correct when the requirement is ad-hoc querying of files already landed in ADLS Gen2.
- ✗
Azure Synapse Pipeline with Mapping Data Flow
Why it's wrong here
Mapping Data Flows run in Spark but are batch-oriented; streaming sources require the continuous execution model, so exactly-once checkpointing into Delta is not delivered here. Data Flows are correct for scheduled batch transformations with a visual designer over bounded datasets.
- ✗
Azure Stream Analytics
Why it's wrong here
Incorrect. Azure Stream Analytics can process streaming data from Event Hubs and output to ADLS Gen2, but it does not natively support Delta Lake as a sink with exactly-once semantics. Stream Analytics offers at-least-once semantics by default, and exactly-once is not guaranteed for Delta Lake outputs.
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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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