Apache Spark: Apply & Evaluate Big Data Workflows

О курсе

Learn the fundamentals of Apache Spark and build a strong foundation in big data processing and distributed computing. This beginner-friendly course introduces Spark's architecture, core components, and Resilient Distributed Datasets (RDDs), then guides you through advanced RDD transformations, persistence strategies, Pair RDD operations, and working with data formats such as CSV, JSON, Parquet, and Avro. Designed for beginners who want to understand scalable data processing, this course combines conceptual explanations with hands-on examples to help you confidently explore Spark's core APIs and workflows. You'll learn how Spark uses distributed and in-memory processing, perform data transformations and actions, optimize applications through persistence, and evaluate storage formats for efficient data handling. By the end of the course, you'll be able to analyze Apache Spark applications, evaluate data storage strategies, and develop scalable data processing workflows using core Spark concepts and APIs. Whether you're starting your journey in big data or building a foundation for Spark-based analytics, this course provides a structured path to understanding essential Apache Spark workflows.

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