You must have heard that Hadoop training can offer you a lucrative career with immense opportunities in Big Data. What you did not know that Hadoop framework comes with its own set of complexities and challenges. Keep reading this article as I take you over the pitfalls and hurdles that you might face before, during or after your Hadoop training.
Leveraging the learning curve
Like every other domain and sector, the web is brimming with resources to help you analyze your learning. While a Hadoop training course will teach you the basics, you can speed up your learning through open-ended community discussions and leverage your learning with available resources online.
However, all of these come into the picture only after you have started learning. Before you begin with your Hadoop training, it is important to analyze what technologies you want to learn and why. You can check our previous article on choosing a Hadoop training course to give a direction to your thoughts.
I will assume that you are decided upon getting a Hadoop training. So, what is the real picture?
Hadoop is a paradigm-shifting technology that equips you to do things like data compilation and analysis. Mind you, here it is not a single data set but vast stores of data. Seemingly impossible, but knowledge in Hadoop makes it otherwise. The next obvious question: What to analyze? It can be anything – it can be customer behavioral patterns, or personalized ad targeting, or creating a marketing budget.
The argument that Hadoop helps in data analysis and in making an informed business decision is not enough to vouch for this technology. Other technologies have been in the market that does the same thing. Then why do Hadoop training? Why not learn other techniques?
The answer to this persisting question is that Hadoop performs all these tasks in minutes or hours with no cost (or little cost) as compared to other technologies. When you club Hadoop with twice as many worker nodes, your processing time increases 2X. Hadoop, over time, has become the single business solution when businesses are looking for fast and reliable processing of large data sets without exhausting their budget.
For instance, did you know The New York Times use Hadoop to convert approximately 4 million entities to PDF in less than 36 hours? Yep. Hadoop is that fast!
Analyzing the Hadoop training course material
Before you enroll for a Hadoop training course, it is important you go over the course materials to be sure that it covers the important facets of Hadoop. Although I am sure every other Hadoop training course material follows the architectural model of Hadoop, it is better to be sure.
To put it more simply, HDFS and MapReduce are two facets of Hadoop that make up this whole technology. HDFS or Hadoop Distributed File System helps in splitting, managing and distributing large chunks of data. These could be in single file or directories. HDFS saves you from handling data back and forth across the network.
MapReduce spins each worker node to which the data is loaded. It can either Map (i.e., assign data based on locality) or Reduce (i.e., aggregate the output). These two programs work together to produce a sensible final output.
Any course material that does not focus on these two deeply may not benefit you in the longer run.
Next comes the OS and programming requirements. I am afraid, Hadoop training is not for someone who has no idea about programming or is blank regarding various OS.
Having said that, Hadoop is written in Java; so is Map and Reduce functionalities. If you Java is rusty, you might first want to brush it up before you start with your Hadoop Training (especially your Object Oriented Skills).
For your knowledge, Hadoop runs well on Windows OS, but originally Hadoop was built on Linux. You must know that the Cloudera Distribution of Hadoop (CDH) is officially only supported on Linux derivatives like RedHat and Ubuntu. So, if you want to excel with Hadoop, you might want to first go over the Linux derivatives. In case you don’t know where to start, you can opt for a quick (but thorough) read of any Linux for dummies books.
For any other queries or confusions, you can write to us directly or post it in the comments below.