Who Can Learn Big Data and Hadoop?

Did you know that top tech firms like IBM, Microsoft and Oracle have all successfully incorporated Hadoop, as their software programming environments last year? While this may be definitely enlightening, these aren’t the only firms vying for professionals with expertise in Hadoop. Some of the other big names looking for Hadoop professionals are Amazon, eBay, Yahoo, Hortonworks, Facebook and so on. No wonder the professional training institutes like Imarticus Learning, which provides excellent, comprehensive training courses in Hadoop are becoming well sought after lately. This may be because Hadoop happens to be among the top ten job trends in the current time period.

While anyone who is an intelligent technologist, can very easily pick up the skills for Hadoop, there happen to be certain pre-requisites that a candidate must fulfil. While there happens to be no hard and fast rule about knowing certain tricks very well, but it is kind of mandatory for a candidate to at least know the workings of Java and Linux. This does not mean an end of the career, for those who aren’t well versed in this programming software. A candidate can very well be a multitasker and learn Big Data and Hadoop, at the same time spending a few hours in learning the ropes of both Java and Linux. While knowing Java is not strictly necessary, but helps you gain a profitable edge over your contemporaries and colleagues.

There happen to a few tools like Hive or Pig, which are built on top of Hadoop and they happen to offer their very own language in order to work with data clusters. For instance, if a candidate wishes to write their own MapReduce code, then they can do so in any programming language like Python, Perl, Ruby or C. The only requisite here is that the language has to support reading from a standard input and writing to a standard output, both with Hadoop Streaming. There also happen to be high-level abstractions, which are provided by the Apache frameworks association, like the aforementioned Pig and Hive. These programs can be automatically converted to MapReduce programs in Java.

On the other hand, there are a number of advantages of learning Java. On one hand, while you can reduce the functions in your language, but there are some advanced features that are only available via the Java API. Often a professional would be required to go in deep into the Hadoop code, to find out why a particular application is behaving a certain way or to find out more about a certain module. In both of these scenarios, knowledge of Java comes in really handy.

There are a number of career opportunities in the field of Hadoop, from roles like Architecture, Developers, Testers, Linux/Network/Hardware Administrator and so on. Some of these roles do require explicit knowledge about Java, while others don’t. To be considered an expert of Hadoop, and be recognised as a good data analytics professional, you must learn the inner workings of Java.

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