Python for Data Engineering
Core Python for data engineers — collections, file I/O, OOP, decorators, generators, and real-world scripting patterns.
PySpark / Apache Spark
End-to-end PySpark — DataFrames, transformations, aggregations, joins, window functions, performance tuning, and Spark Streaming.
Apache Airflow
Airflow from scratch — DAG design, operators, sensors, XComs, task dependencies, scheduling, and production best practices.
Data Modelling
Dimensional modeling, star & snowflake schemas, data vault, normalization, and real-world warehouse design patterns.
Real-World Projects
End-to-end data engineering projects — ingestion, transformation, orchestration, and serving — built from scratch.
DSA for Data Engineers
Arrays, strings, two-pointer, sliding window, heaps, graphs, dynamic programming — curated for data engineering coding rounds.
Interview Questions & Answers
Commonly asked DE interview questions — SQL, Python, Spark, system design, and behavioral — with detailed answers.