DP-203 Develop data processing Practice Question
Your team is developing a data processing solution that uses Azure Databricks to transform streaming data from Azure Event Hubs. The transformation includes joining the stream with a static reference table stored in Azure Data Lake Storage Gen2. You need to implement the join efficiently. 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 a broadcast join with the static DataFrame loaded from Delta Lake
When joining a streaming DataFrame with a static reference table, Spark Structured Streaming can optimize the join by broadcasting the static DataFrame to all nodes, avoiding shuffles. This is efficient since the reference data is small and static. Option A is wrong because watermarks are used for stream-stream joins, not streaming-static joins. Option C is wrong because foreachBatch with a batch join adds unnecessary complexity and does not leverage built-in optimizations. Option D is wrong because converting a static table to a stream is not appropriate; the reference table is static and should be read as a batch DataFrame.
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 a watermark on both sides and perform a stream-stream join
Why it's wrong here
Only one side is a stream.
- ✓
Use a broadcast join with the static DataFrame loaded from Delta Lake
Why this is correct
Broadcast join avoids shuffling and is efficient for streaming-static joins.
- ✗
Use foreachBatch to micro-batch the stream and perform a batch join
Why it's wrong here
Adds complexity without benefit over broadcast join.
- ✗
Use a stream-stream join by converting the static table to a stream
Why it's wrong here
Unnecessary and inefficient.
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