Speed It Up

Now, imagine the massive flow of data from our delivery app—every rider’s turn, every customer rating, every delayed order—stored safely across multiple machines using HDFS. But storing data is just the first step. The real value comes from processing that data quickly and efficiently to gain useful insights.

This is where Apache Spark comes in. Unlike older tools that process data more slowly, Spark is built for speed. It can handle huge datasets by breaking tasks into Spark jobs and running them in parallel. What really makes Spark stand out is its ability to keep data in memory during processing, cutting down the time it takes to read and write from disks.

Introducing Apache Spark

Apache Spark is an open-source, distributed computing system built to handle big data processing more efficiently than older technologies like MapReduce.

MapReduce works by reading data from the hard drive, doing a bit of processing, then writing it back, repeatedly. Imagine having to pause and put your tools away after every little step. It gets slow, especially when there are many steps.

Spark changes the model by keeping everything ready and handy in memory, so it can work through complex tasks and run things like machine learning much faster. It’s like going from walking to zooming on a skateboard—a major speed boost for Big Data projects.

Spark jobs

At a high level, Spark works by running jobs. A Spark job is simply a data processing task you want to complete, like ...