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In the fast-evolving world of big data, the ability to process streaming data in real time is essential. This course is meticulously designed to take you from the basics of Spark and Scala to advanced real-time data processing with Spark Streaming. We begin with a foundational setup of your development environment, ensuring you are equipped to run Spark and Scala on your desktop. A hands-on activity will introduce you to the excitement of live data by streaming and analyzing real-time Tweets. As we move forward, you鈥檒l gain a solid understanding of Scala, a language integral to working with Spark. This crash course in Scala covers the essentials: variables, data structures, and flow control, with practical exercises to cement your understanding. With a firm grip on Scala, you鈥檒l delve into the core concepts of Spark, including the Resilient Distributed Dataset (RDD), which forms the backbone of Spark Streaming applications. We will then explore Spark Streaming in detail, from its architecture to fault tolerance mechanisms, using engaging examples like tracking Twitter hashtags and analyzing Apache logs. Finally, the course pushes the boundaries of your knowledge with advanced topics such as integrating Spark Streaming with Kafka, Flume, and Cassandra. You'll also tackle stateful information tracking, real-time machine learning with K-Means clustering, and deploying your applications on a real Hadoop cluster. By the end of this course, you鈥檒l not only understand the theory behind Spark Streaming but will have the practical experience to apply it effectively in production environments. This course is ideal for software developers, data engineers, and data scientists with a basic understanding of programming concepts. Prior experience with Java, Python, or any object-oriented programming language is recommended but not required. Familiarity with big data concepts will be helpful but is not mandatory.