DP-203 Develop data processing Practice Question
You are building a data processing solution using Azure Databricks. The solution must process streaming data from Azure Event Hubs, join it with a static reference table stored in Azure Data Lake Storage Gen2 (Parquet format), and write the output to Azure Synapse Analytics. The reference table is updated daily. Which approach minimizes latency and ensures data consistency?
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 streaming join and cache the reference table as a static DataFrame.
Using Spark Structured Streaming with a streaming join and caching the reference table as a static DataFrame minimizes latency and ensures consistency by reading the reference table once and caching it for the duration of the stream. This avoids reloading the reference table in each micro-batch (as in option C) and avoids the latency of batch processing (as in option D). Option B, foreachBatch, is useful for writing to sinks like Synapse but does not address the join performance or consistency of the reference data.
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 a streaming join and cache the reference table as a static DataFrame.
Why this is correct
Caching the reference table as static minimizes latency and ensures consistency.
- ✗
Use Spark Structured Streaming with foreachBatch to write to Synapse.
Why it's wrong here
foreachBatch does not address the join latency.
- ✗
Use Spark Structured Streaming with a streaming join and load the reference table in each micro-batch.
Why it's wrong here
Loading per micro-batch increases overhead.
- ✗
Use a batch job that runs every hour to process the data.
Why it's wrong here
Batch processing adds latency and is not suitable for streaming.
Go deeper
Related to this question
Learn chapter
Introduction to Azure Data Engineering
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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