PDE Ingesting and Processing the Data Practice Question
A data engineer is using Spark on Dataproc to process a large dataset. They notice the job is slow due to excessive shuffling. They want to optimize the job by using a more efficient data structure that reduces serialization overhead and provides better memory management. Which Spark API should they 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
✓
DataFrames or Datasets
Spark DataFrames/Datasets use Tungsten execution engine, which provides optimized serialization and memory management. RDDs lack these optimizations. Spark SQL is a module, not an API. Spark Streaming is for streaming.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Spark SQL
Why it's wrong here
Spark SQL is a component, not a data structure; it works with DataFrames.
- ✗
Spark Streaming
Why it's wrong here
Spark Streaming is for streaming data, not a data structure for batch processing.
- ✗
RDDs
Why it's wrong here
RDDs are low-level and do not benefit from Catalyst/Tungsten optimizations; they have higher serialization overhead.
- ✓
DataFrames or Datasets
Why this is correct
DataFrames and Datasets use the Catalyst optimizer and Tungsten execution engine, improving performance and memory efficiency.
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