Start by outlining the end-to-end pipeline: local Spark setup, data ingestion, transformation steps, and output writing. Then dive into specific design choices, such as schema handling, transformation logic, and file format trade-offs, while emphasizing data quality and performance considerations.
Pro tip: Demonstrate production awareness by discussing how you would handle bad data (e.g., quarantine or logging) and ensure idempotency, rather than just focusing on the happy path.
Explain how to set up a local Spark environment, including installing Spark, configuring a session (e.g., SparkSession), and managing dependencies.
Describe reading CSV or JSON files with Spark, including schema inference or explicit schema definition, and handling corrupt records.
Outline a series of transformations such as filtering, deduplication, type casting, handling nulls, and deriving new columns, using Spark DataFrame operations.
Explain writing the transformed data back to disk, choosing appropriate formats (e.g., Parquet), partitioning strategies, and write modes (overwrite, append).
Discuss how to validate the output (e.g., row counts, schema checks) and monitor the pipeline for errors or performance bottlenecks.
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