DP-203 Develop data processing Practice Question
You are developing a data processing pipeline in Azure Databricks that processes streaming data from Azure Event Hubs. You need to ensure that the pipeline can recover from failures and process data exactly once. The pipeline writes to Delta Lake. Which approach should you use?
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 Structured Streaming with a Delta Lake sink and specify a checkpoint location on Azure Data Lake Storage Gen2.
Using Structured Streaming with a Delta Lake sink and specifying a checkpoint location on Azure Data Lake Storage Gen2 enables exactly-once processing. Delta Lake's ACID transactions guarantee idempotent writes, and checkpointing stores stream offsets for recovery. Option A is incorrect because Azure Stream Analytics does not integrate natively with Delta Lake. Option B is incorrect because while foreachBatch can be used for custom processing, the direct Delta Lake sink with checkpointing is the recommended approach for exactly-once semantics. Option D is incorrect because Auto Loader is for batch ingestion from files, not streaming from Event Hubs.
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 to process the stream and output to Delta Lake via Azure Data Lake Storage Gen2.
Why it's wrong here
Does not provide exactly-once semantics with Delta Lake.
- ✗
Use Structured Streaming with foreachBatch to write micro-batches to Delta Lake, and set the checkpoint location to Azure Data Lake Storage Gen2.
Why it's wrong here
foreachBatch may not maintain exactly-once semantics across batches.
- ✓
Use Structured Streaming with a Delta Lake sink and specify a checkpoint location on Azure Data Lake Storage Gen2.
Why this is correct
Provides exactly-once semantics with checkpointing.
- ✗
Use Auto Loader to ingest streaming data from Event Hubs and write to Delta Lake with checkpointing.
Why it's wrong here
Auto Loader is for batch ingestion, not streaming.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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 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.
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