Build a strong foundation in Apache Spark with Scala and learn how to develop scalable big data applications from core concepts to real-time data processing. This course introduces Spark architecture, its integration with YARN, and essential Scala programming concepts, including variables, functions, loops, collections, traits, abstract classes, exception handling, and access modifiers.

As you progress, you'll work with Resilient Distributed Datasets (RDDs), differentiate transformations and actions, and implement Spark Streaming with windowing and checkpointing for fault-tolerant real-time data processing. You'll also construct a Spark project using Maven, integrate external APIs such as Twitter, and evaluate Hadoop 1.x and 2.x for effective resource management in Spark environments. Designed for aspiring data engineers, big data developers, and learners interested in distributed data processing, this course combines Scala programming with practical Apache Spark workflows. By the end of the course, you'll be able to apply Scala fundamentals, optimize RDD operations, implement reliable streaming pipelines, and build end-to-end Spark applications for scalable data analysis.