A. Core Data Engineering Concepts SQL (joins, window functions, performance tuning) Data Modeling (star vs snowflake, normalization) ETL/ELT pipelines (batch vs streaming, orchestration tools like Airflow) B. Apache Spark / PySpark Catalyst Optimizer & Tungsten Narrow vs Wide transformations Joins (broadcast, sort-merge), Skew handling AQE (Adaptive Query Execution) Partitioning, Predicate Pushdown Execution Plan (DAG → Stage → Tasks) Spark UI and Job Debugging SCD Type 2 Implementation in PySpark C. AWS S3, Glue, Athena, Lambda, EMR, Redshift Event-driven design (S3 → EventBridge → Lambda) Security: IAM roles, bucket policies, encryption CI/CD in AWS (CodePipeline, CloudFormation) D. Python Writing modular, reusable code Working with Pandas, Boto3 (for AWS interaction) Exception handling, logging Lambda functions and decorators E. Kafka / Streaming Kafka topic partitioning, consumer groups Offset management Integration with Spark Structured Streaming
