Spark Masterclass with Azure Databricks

This course provides a complete, hands-on understanding of building scalable data engineering solutions using Apache Spark on Azure Databricks. It begins with core Spark fundamentals, including architecture, driver and executor roles, DAG creation, lazy evaluation, and how jobs, stages, and tasks are executed internally.

Learners then move into working with DataFrames and Spark SQL, covering transformations, actions, and writing optimized queries using PySpark. The course dives deeper into Spark internals, including Catalyst Optimizer, execution plans, partitioning, and parallelism. Performance tuning is a key focus, with concepts such as caching, persistence, broadcast joins, shuffle optimization, and predicate pushdown explained with real-world scenarios.

It also introduces Delta Lake fundamentals, including ACID transactions, schema enforcement, and time travel. Azure-specific topics include working with Azure Data Lake Storage (ADLS), cluster creation and management in Azure Databricks, job scheduling, and notebook workflows.

The course includes end-to-end project implementations, simulating real-time data pipelines and batch processing systems. By the end, learners gain practical skills required for production environments, along with interview-oriented knowledge aligned with Databricks certification and real-world data engineering roles.