PySpark courseLesson 1 of 6
PySpark course · Lesson 1 of 6
PySpark Fundamentals for Data Engineers
The PySpark fundamentals in one guide: DataFrames and schemas, lazy evaluation, joins, window functions, UDF alternatives, partitions, shuffles and how to debug performance.
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PySpark lets you write distributed data processing in Python. The API looks like SQL or pandas, but code runs lazily across a cluster, so correctness and performance depend on understanding a few core ideas.
1. DataFrames and schemas
A DataFrame is a distributed table with a schema. Define schemas explicitly in pipelines, choose a parse mode for malformed records deliberately, and remember that Spark 4 enables ANSI mode by default, so invalid casts raise errors.
Read: DataFrames and schemas
2. Lazy evaluation
Transformations build a plan; actions run it. Spark optimises the whole chain before executing. Narrow transformations stay within a partition; wide ones shuffle data and start a new stage.
Read: Transformations vs actions · Practise: Transformation vs action, What causes a shuffle
3. Joins
Join on column names to avoid ambiguous columns, use left_anti and left_semi for existence checks, verify key uniqueness, and let small tables be broadcast.
Read: Joins and join strategy · Practise: Broadcast joins
4. Window functions
Ranking, previous-row comparisons and running totals work as in SQL. Always partitionBy, or Spark moves all data to one partition.
Read: PySpark window functions
5. Built-ins before UDFs
Built-in functions run inside Spark’s engine and are optimised; Python UDFs add serialisation and hide logic from the optimiser. Prefer built-ins, then pandas UDFs.
Read: UDFs and safer alternatives · Practise: When to avoid Python UDFs
6. Partitions, shuffles and skew
Partitions decide parallelism; shuffles are the main cost; skew makes a few tasks run far longer than the rest. Adaptive Query Execution helps with partition sizes, join strategy and skewed joins.
Read: Partitions, shuffles and skew · Adaptive Query Execution
7. Debugging with the Spark UI
Find the slow stage, compare median and maximum task times, check shuffle sizes and spills, and read the executed plan.
Read: Jobs, stages and tasks
Learning order and checkpoints
| Step | You are ready to move on when you can… |
|---|---|
| DataFrames | Read a file with an explicit schema and explain the parse mode |
| Laziness | Point to where the shuffles are in a job |
| Joins | Choose between broadcast and sort-merge and explain why |
| Windows | Write top-N-per-group in PySpark |
| Performance | Diagnose skew from the Spark UI |
Practise end to end with the large-scale batch processing project and revise with the PySpark cheat sheet.
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