Do you realize how much data is floating on the digital medium today? The fact is that social media, online transactions, GPS enabled data from personal devices, customer enrollments, feedback systems, digital sensors – all are uploading data to the web!
So far so good, but what’s the use of data, if it cannot be useful enough? This form of data is not ‘one size fits all’. Imagine how many types of database schemas will be designed to store the various types of data. Can there be a generic template defined for say video clips of varying lengths and specifications?
Capturing the Goliath of data? Mammoth task!
Maybe the best ways to store this data is in its raw form – and not compartmentalize it into pre-defined structures. Yes, that’s it! The ‘unstructured data’ is what is called Big Data in technical terms. Big because of its huge volume! Big because it has tremendous potential to unleash the deepest of insights that businesses can leverage to their advantage.
I mean it is out there, all you’ve got to do is grab it, right?
If only it was that simple. It’s a jungle of data out there!
Picture this! Unstructured data in multiple forms needs to be fed into software intelligence programs and systems to make some useful analysis for businesses. Of course data mining is based on this concept.
Practically on paper, you need a platform to structure the Big Data and this is where Hadoop comes in. Originally designed to be a web search algorithm, it is perfect for the Big Data scenario, since it distributes data and applications on commodity servers in the form of clusters. Concurrent processing of huge volumes of data is its USP, so as the flexibility, scalability and low cost. The fact that it is supported by a community of technical experts and is not the proprietary of any one brand (yes, it is open source software!) means it is vibrantly up-to-date with the changing trends of the market! Calling themselves the Apache Software Foundation (ASF), this is a not-for-profit Hadoop ecosystem.
All’s hunky-dory? Perhaps not!
Like the Murphy’s law Hadoop has is not without its own difficulties:
The experts who know Hadoop are limited in number. So if companies need to adopt Hadoop, resources are not easily available. The iterative algorithms used are not effective as this concept is file dependent. Data analytics use iterative algorithms extensively, so Hadoop poses some problems there. The very advantage of Hadoop distributing data over multiple servers also poses the risk of data security, which is a big challenge for practical adoption of the technology. The available tools for the management of data and smart governance are also limited.
Is that a deterrent? No!
Challenges pose a motivation for innovation! People are trying to break the ground to overcome the problems with Hadoop as they try to unleash the full-scale potential of Big Data. New tools and techniques are emerging that are trying to address the risks and hurdles.
Toying with the idea of using Big Data for your enterprise? Have any questions and doubts in your mind? Just reach out to us at firstname.lastname@example.org and our experts in Big Data will be delighted to help you!