Big Data with Spark & Hadoop introduces you to the powerful ecosystem of tools used to process, analyze, and manage massive datasets. Designed for beginners and aspiring data professionals, this course provides a clear, practical foundation in distributed computing and big data architectures.
 
You’ll learn how Apache Spark and Hadoop work together to handle data at scale, exploring core concepts such as distributed storage, parallel processing, resilient data operations, and large-scale analytics. Through hands-on exercises, you’ll gain experience using Spark for fast, in-memory computation and Hadoop for reliable, fault-tolerant data management.
 
By the end of the course, you’ll understand how big data systems operate, how modern organizations leverage them for insights, and how to start building scalable data-processing workflows of your own.
 
What You’ll Learn
Core concepts of big data and distributed computing
How Hadoop’s ecosystem enables scalable data storage and processing
Using Apache Spark for batch processing, transformations, and analytics
Working with RDDs, DataFrames, and Spark SQL
Designing efficient workflows for large-scale data processing
Practical skills to prepare for more advanced big data engineering and analytics courses