Why Hadoop is perfect for managing BIG data
Anyone that’s spent any amount of time researching or reading about BIG data will have come across the term “Hadoop” at some point. In simple terms, Hadoop is a project with aims of developing open-source software for use in analysis, processing and distribution for BIG data.
How does Hadoop work, exactly? If you can imagine software being used, in an application layer, to scale up or down as necessary (this could include multiple servers and untold numbers of individual machines), then you’re getting the right idea. The project itself is chiefly composed of 4 separate modules, each with its own specific area of functionality:
From the Apache Hadoop site:
Hadoop Common -The common utilities that support the other Hadoop modules.
Hadoop Distributed File System – A distributed file system that provides high-throughput access to application data.
Hadoop YARN – A framework for job scheduling and cluster resource management.
Hadoop MapReduce – A YARN-based system for parallel processing of large data sets.
Believe it or not, the technology which is driving Hadoop isn’t necessarily completely original; in fact, the Hadoop project is descended from a project called “Nutch”. But the story doesn’t end there, the Nutch project is actually a slight refinement of technologies which Google created for use in large-scale indexing of content. Hadoop has some special abilities when it comes to handling large data sets; for example, it’s very good at processing both structured and unstructured data. In other words, we’re talking about deep analysis and organization here which wouldn’t normally be possible with different degrees of structured data.
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Basically, you can run Hadoop on hundreds of servers which share no resources (memory) and it will magically organize your data. If you have a number of servers running with the software installed, it will redistribute your bulk data across all these servers in an effort to organize it. Hadoop is also very safe in terms of potential data loss scenarios; like in most cloud computing setups, data is copied across to multiple servers allowing for increased security and quick recovery in the event of loss or catastrophic failure.
At this point you’re probably wondering where Hadoop might be used, right? The real question is, are there any large scale data processing projects that you can’t use Hadoop for? Online retailers can benefit from using Hadoop; it might allow them to present specifically targeted search queries and ads to the right customers at the right times. Likewise, it would even be possible to use Hadoop to break down and analyze a large amount of customer purchase records (in an effort to boost sales), perhaps highlighting some area that is being neglected. But Hadoop can also be used to help break down data and identify patterns in other areas as well; finance is an oft referenced example in this regard.
In many ways, Hadoop is a software solution / approach to BIG data management that functions in a very similar manner as cloud computing. How’s that? Well, given that both Hadoop and the cloud are essentially elastic, scalable technologies which are driven largely by software, and have the ability to control and requisition computing power from multiple machines…isn’t it fairly obvious? Because these two technologies exhibit close similarities and are compatible, it’s well within reason to assume that they will likely merge to some extent. After all, the proliferation of exceedingly large amounts of data continues unabated, cloud computing is “on deck” to replace grid computing, and people are going to need solutions for crunching / organizing BIG data. Then of course there’s the realization that (in many ways) cloud computing is simply another way of approaching or taking advantage of certain forms of BIG data. Many big businesses are already installing Hadoop into their own IaaS; surely this is a sign of bigger things to come.
In short, while there are different ways of managing BIG data, Hadoop presents one of the most affordable, customizable and available solutions attainable. Additionally, because its open-source, we’re likely to see notable improvements and upgrades arriving for quite a long time at little to no extra cost(s).