Big Data Processing with Hadoop and Spark

Organizations today generate vast amounts of data that traditional databases cannot handle. Understanding how to store, process, and analyze this information at scale is a critical skill for modern data professionals.\n\nThis text-based course guides you from big data novice to a confident practitioner capable of managing large datasets. You will learn how to design data pipelines, write distributed queries, and leverage modern cloud-ready data architectures.\n\nWhat you'll learn:\n- Understand the core concepts of distributed storage and computing, starting with Hadoop HDFS and MapReduce.\n- Process large datasets efficiently using Spark RDDs and Spark SQL.\n- Apply PySpark for writing scalable data processing scripts in Python.\n- Implement structured streaming concepts to handle real-time data feeds.\n- Explore modern data lakehouse patterns and Delta Lake storage formats for reliable data pipelines.\n- Query and analyze distributed data using Hive and Spark SQL.\n\nYou will start with the fundamental terminology of distributed systems before progressing to hands-on written exercises where you will read code examples, write queries, and build data processing logic. The course wraps up with modern storage formats and real-time streaming paradigms used in industry today.\n\nDesigned for beginners, this course requires no prior experience with big data systems. A basic understanding of programming concepts and SQL is helpful but not required.\n\nStart your journey into big data engineering today.