The Intended Audience and Prerequisites
Big Data and analytics are some of the most envied jobs of our generation. The simple reason for this being today there is an urgent need for Big Data and Hadoop professionals regardless of the organization’s industry segmentation or vertical. So this Tutorial is intended towards those individuals who are awed by the sheer might of Big Data and the influence that it commands in corporate boardrooms and thus naturally would want to take up a career in Big Data and Hadoop.
So as an individual you need to go through this Tutorial if you aspire to be a Big Data and Hadoop Developer, Administrator, Architect, Analyst, Scientist, Tester and such other professional roles. This Tutorial is also an essential guide and roadmap for those business decision-makers who keenly want to understand Hadoop and MapReduce, and they could be having a corporate designation such as Chief Technology Officer, Chief Information Officer, or even a Technical Manager of any enterprise. Thus a basic level of technical proficiency goes a long way in strengthening your prospects of making it successful in Big Data and Hadoop.
As far as prerequisites are concerned, there are no such hard and fast rules to have a successful career in Big Data and Hadoop. But if you have a prior knowledge of Java and Linux programming then you can get a head start in this field. But if you don’t, then fret not since we also provide you a basic course on Java in order to assist you in your understanding and learning of Hadoop.
Apache Hive and Pig are tools that are created as a layer on top of Hadoop and as such they offer their own high-level programming languages. So there is no strict necessity of Java or Linux either. Also, it is possible to create your own MapReduce program in any programming language like Ruby, Python, Perl and even C programming. Thus you can clearly see that what is needed is a mindset that understands computer programming logic and deductions. Everything else is an add-on and can be easily assimilated in a short duration of time.