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What Can Be Done With Hadoop?


Hadoop allows you to work on massive amounts of data simultaneously. Even terabyte or petabyte-scale data sets that would normally be far too big to fit on a single disk can be taken on. Hadoop uses a distributed file system that breaks up files and spreads them across a network of servers or nodes. This means you can work on any dataset easily, no matter how massive.


With distributed storage and processing, you can scale up your Hadoop operations to handle more data by simply adding new servers to the network. To scale back down again, just remove them – no complex administration or additional setup will be required. Hadoop also runs on cheap and available commodity hardware, so you won‘t need to worry about buying special equipment.


Even the world’s most powerful and expensive supercomputers can‘t process the volume of data being generated in today‘s business world. Hadoop uses parallel processing to solve this problem in a different way. It breaks down massive problems and shares the load between dozens, hundreds, or even thousands of smaller computers. This allows data tasks that would normally take hours or days to be solved in seconds.


Essentially unlimited storage and processing power at a low cost per unit means you can store more detailed data than ever, and process all of it with heavyweight machine learning techniques. You‘ll be able to get much more value out of your current datasets and data streams and gain actionable business insights and predictions.


Hadoop allows you to store and analyze any kind of data or file. Small, large, text, binary, images, or anything else. The Hadoop file system is schemaless, meaning it‘s great for storing structured, unstructured, semi-structured, relational, and non-relational data. Whatever you need to process, Hadoop can handle it.

Benefits of Hadoop


Hadoop has built-in fault tolerance, so your data and applications won‘t crash because of a hardware failure. When using many machines to simultaneously store and process data at scale, the chances of hardware failures increases. Hadoop deals with this by storing multiple copies of files across different machines, and will quickly and seamlessly recover if something goes wrong.


Hadoop is all about keeping you in the driver seat. You won‘t need to preprocess data before storing it, and you can store as much data as you want and decide what to do with it later. Hadoop lets you swap out almost any of its components for different software tools, so you and your developers can set things up however you like.


Hadoop is an open-source framework, so it‘s free to use and there are no licensing fees or commissions. Also, the ability to use commodity hardware means you can build a powerful system able to store and process large amounts of data at a very low cost.

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