What do finance, media and entertainment, government, retail and healthcare industries with Big Data requirements have in common? A lot of them are now using Hadoop, a free, Java-based programming framework that supports the processing of large data sets in a distributed computing environment.
Finding the framework to process Big Data can be a hassle. However, Hadoop has achieved tremendous popularity. It’s cost effective, efficient, doesn’t require structured data and, if implemented correctly, is fault-tolerant.
Even juggernauts such as Google and IBM use Hadoop for applications that involve search engines and advertising.
- It works with Windows and Linux operating systems but it can also work on BSD and OS X.
- Hadoop is one of the most popular frameworks for large-scale, data-intensive deployments. Hadoop can process large amounts of data from terabytes to petabytes and beyond.
- Hadoop is designed to efficiently process huge amounts of data by connecting many commodity computers together so they can work in parallel, thus solving the problem of having too much data fit onto one machine.
- Hadoop doesn’t require you to structure the data you input to it. Users can literally dump their information into the framework without needing to reformat it. (It does take some data cleansing but at least it doesn’t require schema to schema transformations).
- It’s cost effective because it controls costs by storing data more affordably per terabyte (compared to other frameworks). Instead of spending thousands or tens of thousands per terabyte, Hadoop delivers compute and storage services only for hundreds of dollars per terabyte.
- According to experts, Hadoop’s fault tolerance is one of its most important advantages. Even if individual nodes experience failure, data is replicated across a cluster so that it can be recovered easily in the face of different types of failures (disk, node or rack).