Over the last few years both these horses have come on top in the IT World Derby, and we need no introduction for either of these Popular players. Yes, we are talking about the rise of Big Data & Cloud Computing. We are all familiar with these concepts(but you can still click on the links to know more). Big Data has existed before, and the Big Guns having the Dollar power have been using them. But with the recent developments in the Tech world, even the smaller players are now turning their faces towards Big Data. Also the Data boom over the last few years can only be slayed by Big Data Analytics. If you’re still not convinced about Big Data, take a look at what all happened in the last 60 Seconds while you were Fixing your hair!
[Big Data Life Cycle: Reinventing Big Data with AWS]
A Research Survey by Gartner in 2011 showed how there is a dramatic rise in data generation as compared to data available for analysis, which means that more and more data is flowing out unchecked without analysis. Gone are those days of sampling from a data set to predict trends. The new age is a Data Driven one, where the schema to tackle business processes are now being built on Data itself. With the 3Vs of Data kicking in, larger data sets have to be monitored & analyzed through different parameters for results that can truly drive business decisions.
To tackle data in larger & faster flux through a wide variety of mediums organizations need a ton of tools, resources, finances & time. Even to Set up a Big Data infrastructure may take weeks. You will need a mix of both Infrastructure/hardware scale as well as the software/platforms/ecosystems set up, and then maintained over a period of Time. (Phew, that’s alot and we are not even getting into Hadoop!)
So here, we come across a few challenges, some of which you might have already identified:
• Infrastructure Challenge in terms of Storage & Compute Elements
• Software Challenge in terms of choosing, configuring & maintaining Platforms & Ecosystems
• Man Power Challenge of pooling talented data scientists for setting up & maintaining
• Time Challenge of setting up a Big Data infrastructure & then managing it
• Tons of Maintenance Issues starting from cooling to patching up codes & updates.
• Inelastic Capacity, meaning you lose out on real time data during peak times of business & pay for capacity you are not using for the rest of the year.
So, there is a definite Gap that needs to be filled. But, where does Cloud come into the picture? Well, all the above issues can be seamlessly solved when you’re on the Cloud. We define Cloud computing through its various benefits, right? like:
• Pay per use
• Unlimited Scale
• Managed Services
If you think about it, these help us in just the right way to deal with our Big Data conundrums! Now you can Collect, Store, Analyze & Share (The Big Data life cycle which we spoke about in our earlier blog) your Big Data findings. Also you pay for just the resources you use for the time that you use it for.
A recent Study by IDC, showed that “Over the next decade, the number of files or containers that encapsulate the information in the digital universe will grow by 75x.” While the pool of IT Staff available to manage them will grow only slightly. At 1.5x”. This fact materializes our growing fear. If all our Data Scientists are busy setting up & managing Big Data systems, then it leaves very less time to actually work with the findings and drive innovation.
The Figure above shows how Cloud-Based infrastructure gives your Data wizards the majority of their time in actually putting their Big Data findings to use and innovate.
[Optimize Big Data Analytics with a Complete Guide to Amazon Cloud’s EMR]
What about the Software/ Hardware Conundrums?