Description
Big Data Fundamentals with Hadoop and Spark introduces you to the core technologies that enable the storage, processing, and analysis of massive datasets. This course begins by explaining the challenges of big data and how distributed computing solves them. You’ll start with Hadoop, learning its ecosystem components including HDFS (Hadoop Distributed File System), MapReduce, and YARN. Then, you’ll dive into Apache Spark, the fast, in-memory data processing engine revolutionizing big data analytics. Through hands-on labs, you’ll write Spark applications in Python and explore components like Spark SQL, DataFrames, and MLlib. The course covers batch vs. stream processing, data ingestion with Sqoop and Flume, and orchestration with tools like Oozie and Airflow. You’ll also explore real-world case studies in industries like healthcare, finance, and retail. By the end, you’ll have the knowledge to work with large-scale data in distributed environments and understand how to architect big data solutions. Ideal for aspiring data engineers, analysts, and developers entering the big data space.
Jummai –
The course helped me understand how our tech team uses Hadoop and Spark to process massive datasets. I can now have more informed conversations during planning and implementation.
Benedict –
I loved how the course balanced theory with hands-on exercises. I could immediately apply what I learned to my own data projects. Great intro to the big data ecosystem!
Jafaru –
This course gave me a solid foundation in Hadoop and Spark, which I struggled with in college. The instructor made complex topics easy to grasp, and the hands-on labs were extremely helpful.
Alhassan –
Exactly what I needed to break into big data. The practical examples and real-world use cases helped me understand how Hadoop and Spark power today’s data infrastructure.
Chiamaka –
Clear, concise, and relevant! I had zero experience with distributed computing, but this course built my confidence step by step. The Spark sections were particularly well explained.