Data Engineering Tools

Python API for Apache Spark: when to use it, how it maps to Spark SQL, and where interviewers focus.

From SparkSession to write: DataFrame basics, transformations, and actions.

The PySpark questions that come up in real L4 to L6 data engineering interviews.

Shuffle, skew, AQE, broadcast joins, and the questions that separate seniors from staff.

Advanced Spark: Catalyst, physical plans, executor tuning, and incident debugging.

AI 4-phase Spark interview simulation with code execution and verdict.

PySpark problems grouped by category. Your code runs, and the feedback names the objection an interviewer would raise.

PySpark problems by difficulty, with runnable code and tests.

Broadcast joins, anti joins, and multi-column join patterns.

Shuffle cost, skew, and the aggregation patterns interviewers probe.

dropDuplicates vs window dedup: when each is correct.

Performance notes and NOT IN pitfalls with NULLs.

The functions you reach for in interviews, organized by task.

Spark SQL functions reference for data engineers.

Syntax, execution plan, and use cases for anti joins.