Introduction

In today’s world, data is generated at an incredible speed and scale. Think about social media activity, online shopping, financial transactions, or even sensors in smart devices. All of this information needs to be processed and analyzed to extract insights. Traditional systems, like Hadoop MapReduce, were once the go-to solutions, but they often struggled with speed because they relied heavily on writing intermediate results to disk.

Apache Spark was designed to overcome this limitation. It is an open-source, distributed computing system that processes large amounts of data across many machines while keeping as much as possible in memory (RAM). This design choice makes Spark both fast and scalable, capable of handling anything from small datasets on your laptop to petabytes of data on massive clusters.