Big Data Fundamentals, Apache Spark, and Distributed Systems

Processing massive datasets requires specialized architectures and modern distributed computing paradigms. This course introduces core Big Data principles to help you understand distributed processing and modern data engineering pipelines. You will start with essential terminology, learning the characteristics of Big Data, relational databases versus NoSQL storage solutions, and distributed file systems like HDFS. From there, you will explore Hadoop MapReduce and Apache Spark, including Spark parallelism, execution models, and data transformation workflows. In this course, you will: Learn foundational Big Data concepts and distributed system architectures. Compare relational databases with NoSQL paradigms to select appropriate storage solutions. Understand the Hadoop ecosystem, HDFS operations, and MapReduce processing. Apply Apache Spark principles, DataFrames, and parallel computing techniques. Configure efficient ETL and ELT workflows for continuous data processing. Explore modern data management trends, including cloud data lakehouses and vector storage concepts. The curriculum begins with fundamental terminology before moving into practical architecture patterns. This course is created for beginners and aspiring data specialists with no prior distributed computing background. Read through the lessons to build a strong foundation in modern Big Data engineering.