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Embark on a journey to master big data processing with Apache Spark and Scala. This course begins with setting up your development environment, ensuring you have a solid foundation in both Spark and Scala. You will dive into a Scala crash course that covers syntax, flow control, functions, and data structures, giving you the essential skills needed to work with Spark. Next, you will explore Spark's core concept, the Resilient Distributed Dataset (RDD). Through a series of hands-on activities and exercises, you will learn to manipulate RDDs, implement key/value operations, and perform complex data transformations. The course then transitions into SparkSQL, DataFrames, and DataSets, where you will practice querying structured data efficiently. You'll also tackle advanced Spark programming, where you’ll apply algorithms to real-world datasets, work with clusters, and optimize performance. As you progress, you will delve into machine learning with Spark MLlib and explore how to build recommendation systems, perform regression analysis, and implement decision trees. Finally, the course introduces Spark Streaming and GraphX, allowing you to process real-time data streams and graph-based data efficiently. By the end of this course, you will have the expertise to leverage Spark and Scala for complex data processing tasks in any industry. This course is designed for software engineers who want to expand their skills into the world of big data processing on a cluster. It is necessary to have some prior programming or scripting knowledge.