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PYSPARK
PySpark for Data Engineers
Spark architecture, DataFrame transformations, joins, window functions, UDFs, performance tuning, and production patterns.
9 in-depth tutorials
PySpark foundations: SparkSession, imports, configuration, and your first DataFrame.
Spark architecture: driver, executors, DAG, stages, and Python vs PySpark.
PySpark joins: inner, left, anti, semi, cross, and self-joins with Databricks examples.
DataFrame transformations cookbook: select, filter, groupBy, agg, and column operations.
Master lazy evaluation — the most important PySpark concept nobody explains properly.
Master every PySpark window function with real business scenarios. ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD, NTILE, running totals, moving averages, percent of group.
07PySpark Data Cleaning & Validation
Complete PySpark data cleaning guide. Null handling (check, drop, fill, coalesce), deduplication (dropDuplicates vs window-based keep-latest), type casting with safe conversion, string cleaning (trim, regex replace…
08PySpark UDFs & Higher-Order Functions
Complete guide to PySpark UDFs and higher-order functions. Built-in vs UDF comparison, creating standard Python UDFs, the serialization performance problem (10-100x slower), Pandas UDFs (vectorized 5-10x faster)…
09PySpark Reading from APIs & Databases
Complete guide to reading databases and APIs with PySpark. Basic JDBC read with connection properties, custom SQL query pushdown, parallel JDBC reads (partitionColumn, numPartitions, lowerBound, upperBound), choosing…