What I will learn?

• The architecture and core concepts of Apache Spark
• How to use Spark’s DataFrame and SQL APIs for scalable data processing
• How to build ETL pipelines for batch and streaming data
• How to integrate Spark with common data sources: CSV, Parquet, JDBC, Kafka, and cloud storage
• How to perform advanced analytics and machine learning with Spark MLlib
• How to optimize Spark jobs for performance and resource usage
• How to debug, test, and monitor Spark applications
• How to deploy Spark jobs in production environments (local, YARN, Kubernetes, cloud)
• How to use modern table formats (Delta Lake, Iceberg, Hudi) for reliability and ACID compliance
• Best practices for writing maintainable, efficient, and robust Spark