DP-203 Develop data processing Practice Question
You are a data engineer for a global e-commerce company. You need to design a data processing solution using Azure Databricks that processes real-time clickstream data from Azure Event Hubs. The solution must join the streaming data with a slowly changing dimension (SCD) table that stores product details. The SCD table is stored in Azure Data Lake Storage Gen2 as Delta format and is updated every few hours. The joined results must be written to a Delta table for near-real-time dashboards. The key requirement is to ensure that the join always uses the latest version of the SCD data without reprocessing the entire stream. The solution must minimize latency and cost. Which approach should you recommend?
⚠ Common exam trap
Candidates often assume that reading the SCD table in every micro-batch (Option A or C) is the simplest way to get the latest data, but they overlook the significant performance and cost penalties of full table scans in each micro-batch, especially for large SCD tables.
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 Spark Structured Streaming with a static DataFrame for the SCD table and refresh it periodically using a trigger that reads the latest snapshot.
It uses a static DataFrame for the SCD table and refreshes it periodically using a trigger (e.g., a time-based or file-based trigger). This approach ensures that the join always uses the latest version of the SCD data without reprocessing the entire stream, as the static DataFrame is re-read only when the SCD is updated. It minimizes latency and cost by avoiding the overhead of reading the SCD in every micro-batch (as in Option A) or using a streaming join (as in Option D), which is not suitable for batch-updated SCD tables.
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 Spark Structured Streaming with foreachBatch and read the SCD table inside the foreachBatch function.
Why it's wrong here
Similar to B, inefficient due to repeated reads.
- ✓
Use Spark Structured Streaming with a static DataFrame for the SCD table and refresh it periodically using a trigger that reads the latest snapshot.
Why this is correct
Periodic refresh of a static DataFrame minimizes overhead and ensures latest data.
- ✗
Use Spark Structured Streaming with a batch read of the SCD table in each micro-batch using spark.read.
Why it's wrong here
Reading per micro-batch increases overhead and latency.
- ✗
Use Spark Structured Streaming with a streaming join on the SCD table by converting it to a stream using readStream.
Why it's wrong here
Streaming join on a batch source is inefficient and may cause reprocessing.
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