Data engineering coding practice
PySpark Interview Practice
Prepare for PySpark and Apache Spark interviews with hands-on DataFrame questions, Spark SQL exercises, performance scenarios, and AI-reviewed solutions. The dedicated editor uses PySpark only, keeping every prompt and starter template aligned to data-engineering work.
What you will practice
Begin with SparkSession, DataFrame creation, expressions, filtering, null handling, grouping, and joins. Intermediate and advanced sessions move into windows, complex types, execution plans, partitioning, caching, broadcast strategies, UDF trade-offs, structured streaming, and Delta Lake.
Each question is paired with theory and examples so you can explain not just what code works, but why it scales. Review feedback on transformations, actions, shuffles, serialization, skew, partition sizing, and the practical decisions interviewers expect from a data engineer.
- DataFrame creation, selection, filtering, expressions, and nulls
- Aggregations, joins, windows, arrays, maps, and structs
- Spark SQL, temporary views, schemas, and data formats
- Partitions, shuffles, caching, broadcast joins, and skew
- Execution plans, UDF alternatives, testing, and optimization
- Structured Streaming, checkpoints, watermarks, and Delta Lake
How a practice session works
Step 1
Choose a difficulty, topic, or targeted plan. The engine creates an interview-style prompt with the context and constraints needed to reason about a correct solution.
Step 2
Write and run your answer in the browser. Ask for a focused hint or theory explanation when you need help without immediately revealing the final answer.
Step 3
Review semantic feedback, complexity notes, examples, and the reference approach. Automatic checkpoints preserve the latest question and code for your next visit.