Hello, Hadoop!
Learn how Hadoop stores and processes massive datasets using clusters, HDFS, and MapReduce.
The Hadoop framework was first introduced by Doug Cutting and Mike Cafarella in 2005, inspired by Google's MapReduce and GFS (Google File System) papers.
Let’s go back to that example of a city full of delivery bikes zipping around. Remember how each bike tracks its location, delivery time, traffic delays, and customer feedback—all in real time? Now, imagine that the company expands to 10 more cities. Then 50. Now you’ve got thousands of bikes collecting data every second.
At first, the company stores all data on a central server. It works while the data volume is small. But as data grows, the system slows down. Deliveries get delayed because the app can’t load traffic updates fast enough. Reports take hours to generate. The data continues to grow—and the servers can’t keep up.
This is what happens when Big Data outgrows traditional systems.
In the early 2000s, companies like Google faced similar challenges. Their existing tools weren’t built to handle billions of searches, websites, and users simultaneously. Traditional systems couldn’t store or process the data efficiently. So, they came up with solutions.
Marissa Mayer, a former VP at Google, reported that during an experiment, a half-second increase in load time led to a 20% drop in search traffic and revenue. (
Say hello to Hadoop
To solve this Big Data overload, engineers needed a smarter way to handle massive amounts of information without system crashes or delays. That’s when Hadoop was introduced—a powerful, open-source framework designed to store ...