Do I need Hadoop to become good Big Data Analyst?

Learning Hadoop is undoubtedly getting into big data. It cannot be denied that there are alternatives to Hadoop for Big Data processing. There were long before Hadoop revolutionized the big data landscape with its effective solution at a low cost. However, they were tremendously expensive and their use was limited to some large companies.

When it comes to starting in Big Data it is important to keep in mind that Hadoop is a billion-dollar market, with exponential growth due to its comparative advantages. Undoubtedly, talking about Big Data is talking about Hadoop. Even so, following the thread of our approach, the existence of alternatives makes them a possible option.

A key factor in deciding are the scalability and low cost of implementing a Hadoop cluster. Thus, it will be a good idea to start taking advantage of Big Data using Hadoop as a centralized data repository that increases its processing capacity and at the same time reduces storage costs.

Taking advantage of this possible progressive implementation, our goal can begin with a small project and, as we improve our knowledge, end up becoming a mission critical system. With the added advantage that in the network we can find endless free resources for learning.

On the other hand, if our intention to start in Big Data to carry out a project that requires security control, Hadoop will not be the best option. Although there are solutions that allow you to handle the situation, most likely they are not available to beginners and, in the case of such a tricky issue, you should not take risks. Similarly, it will be inappropriate to decide on Hadoop if our intention is to work in real-time. Although Apache Spark is also viable as a complete alternative.

It is also important to keep in mind that Hadoop has its complexity. Just as Big Data requires mathematical and technical knowledge to determine the appropriate technological options in each case, Hadoop entails its difficulty.

Your learning will not be easy. At present, Hadoop's most complex implementations are mostly found in large companies and in industries where large data is critical to its operation. But its complexity does not prevent taking small steps with a well-defined scope. Moreover, the best way to learn Hadoop is to take Hadoop course from India’s largest E-Learning Hadoop training institute in Bangalore with skilled trainers.

And, of course, before we ask ourselves if we need Hadoop to get started in Big Data, let's consider if really applying Big Data technologies and analytics is a necessity. Let's not forget that both Big Data and Hadoop has its feet on the ground.

Hadoop, the star of Big Data

Learning of Hadoop in Hadoop training institute in Bangalore will be essential from the beginning the case of wanting to introduce ourselves in the sector. If our goal is to be part of the select group of people who have the sexiest profession of the 21st century, as defined by the Harvard Business Review, learning Hadoop from the beginning, it is almost obligatory.

With exceptions, today companies that bet on big data use Hadoop as a framework. Although doing so will discover that the Big Data business environment is not always so seductive. His practical side tends to prevail over the idealist and, in general, the approaches are most prosaic. Essentially, identify a need and assess the return on investment to make a decision about it.

Many are complex, high-risk Big Data projects, so their precise implementation of great specialists. Or what is the same, the learning phase of these professionals is continuous and, without a doubt, Big Data is practically synonymous with Hadoop. 

Its adoption in smaller companies, on the other hand, requires making a simpler Hadoop, with less intensive resources. This does not mean that it is easier, although in both cases having commercial solutions facilitates its implementation. In any case, daring with Hadoop from the beginning is decisive to achieve the specialization required in these areas.