4 Real-World Uses for Getting Your Feet Wet With Hadoop
If you’ve been researching big data, Hadoop has most certainly popped up as a powerful option for analytics. Some blogs and articles (particularly those written by Hadoop vendors) make Hadoop sound like the sliced bread of data analytics. But when you dig deeper, it’s hard to get a handle on exactly what operations Hadoop is best for, as opposed to jobs that Hadoop obviously doesn’t excel at.
Hadoop is extraordinary with extremely large sets of data (think petabytes), and unstructured or semi-structured data. It’s excellent with social media data, mobile data, and data produced by the Internet of Things. What it isn’t good at is speedy (real time) analytics. It can be difficult or impossible to find out who’s using Hadoop and what they’re using it for, because those proprietary secrets are closely guarded. Nobody wants to give competitors the edge by outlining their successes with Hadoop, nor admit failure when a Hadoop initiative didn’t work as planned.
So, what is Hadoop actually good for in real-world practice?
1. Predictive Modeling
What will the market look like in the future? What products will customers be most interested in? Hadoop is strong in predictive modeling.
Predictive modeling doesn’t require real-time analytics. What produces the best predictive modeling is a thorough processing of all of the relevant data sets, which takes time. Businesses that have utilized Hadoop for predictive modeling have been quite impressed with the results. You just won’t usually see them blogging about it, because they don’t want their competitors to know why their customer profiles just got 1,000 times better.
2. Analytics for Deep Insight
Gleaning deep insight is much easier in Hadoop than most other data analytics options. Let’s say that you need insight into what worked particularly well in previous versions of your product, and what about the design didn’t pan out as expected. Perhaps you need deeper insights into what prompts a solid lead to convert to a paying customer. Hadoop is extraordinarily good at providing deep insight, it just doesn’t do it instantly. If you can wait a little while for the answers, Hadoop is your guy.
3. Analytics for High Accuracy
When the analytics has to be on target, Hadoop can provide that. It just can’t do so in real time.
Since Hadoop literally queries all of the data, it is extremely accurate. Hadoop is actually much more accurate than data analytics options that provide real-time insight. When your plans call for higher accuracy, and you’re willing to wait for the answers, Hadoop is ideal for the job.
4. An Extension of the Database, Not a Replacement
Many organizations adopt Hadoop with plans to completely replace their databases. This isn’t the best use for the product. Hadoop isn’t what you want to manage your daily operations, it’s what you need for deep data analysis. Hadoop is excellent at building indexes, recognizing patterns, driving recommendations engines, and conducting analysis on consumer sentiment. It isn’t right for replacing the databases your workforce depends on for daily operations.
When researching real world solutions, you’ll likely find topics related to the difficulty of offloading data from the mainframe (or your current IT infrastructure) and loading it to Hadoop. This is no longer an inhibiting factor in Hadoop adoption, because there are practical and affordable solutions to ingest, translate, process and distribute mainframe data with Hadoop.