PySpark practice

PySpark Interview Practice

Real data-engineering scenarios: skewed joins, streaming pipelines, window analytics and Catalyst tuning. Every solution is graded against Spark 3.5 semantics with feedback on both correctness and performance.

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Topics covered

DataFrame API vs Spark SQL
Joins (broadcast, sort-merge, skew)
Window functions
UDFs & pandas UDFs
Partitioning & bucketing
Catalyst & AQE tuning
Structured Streaming
Delta Lake basics
Checkpointing & watermarks
Schema evolution
Reading Parquet / ORC / Avro
Job optimization patterns

Why PySpark matters for data engineers

PySpark is the default interview language at most data-platform teams (Databricks, Netflix, Uber, Airbnb). Comfort with the DataFrame API, execution plans and skew handling separates senior candidates from junior ones — this playground drills exactly those muscles.

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