PySpark Learning Roadmap

A structured progression for mastering PySpark, from basic DataFrame operations to advanced distributed computing optimization.

Spark Fundamentals

Core concepts of Apache Spark

SparkSessionDriver vs ExecutorsTransformations vs ActionsLazy EvaluationRDDs vs DataFrames vs DatasetsPartitions

Reading, writing, and viewing data

spark.read / spark.writeCSV, JSON, Parquetdf.show()df.printSchema()df.describe()df.columns

Transforming your datasets

select()filter() / where()withColumn()withColumnRenamed()drop()cast()lit()

Summarizing data

groupBy()agg()F.sum(), F.avg(), F.count()F.max(), F.min()F.countDistinct()pivot()

Combining DataFrames

join() (inner, left, right, outer)Cross JoinsBroadcast Joins (F.broadcast)union()unionByName()

Advanced logic and analytics

pyspark.sql.functions (F)Window.partitionBy()F.rank(), F.row_number()F.when().otherwise()UDFs (User Defined Functions)

Tuning Spark applications

Caching (df.cache())Persist (df.persist())repartition() vs coalesce()Spark UIAvoiding ShufflesData Skew Handling