Big Data is the LBD of the computing world in 2017. Everywhere you look there are software platforms, modules and analytical tools that synchronously sing the praise of big data. If you are already contemplating a career in big data then there’s good news for you, the area is sphere is comparatively less explored and there’s a wide range of opportunities to optimize the techniques for mastering big data management before they are commoditized.
But there are a few skills you will need to master before you set out on this arduous but rewarding journey –
i. Apache Hadoop – with two decades in its portfolio Hadoop is entering a new era of exploration. Big data is powerful and there’s no other fussy beast that can properly archive, organize and maintain petabytes of data better than Apache Hadoop.
ii. NoSQL – Oracle and IBM DB2 may have been big till 2013, but with the advancement of scale-out databases like Couchbase and MongoDB the operational side of big data is becoming both more explored and manageable. Hadoop and NoSQL databases are entwined. Many a times, data crunched by Hadoop is sourced from NoSQL databases like the ones mentioned above. These two are the most important components of a perfect data circle.
iii. Data mining – machine learning and data mining are two terms that are almost always found next to each other. Data mining has scaled with big data and as of 2015; machine learning is the virtuous way to mine data. Any professional who can harness machine learning to build and train analytics apps for personalization of systems can get anything between $60,000 to $110,000 USD per month for their services.
iv. Data Visualization – as we already know, there is no better way to analyze data than to get your eyes on it. For big data this can be a little dicey considering the sheer size and dynamicity of the data. Although sometimes an analysis of sample data is enough to understand the shape of your big data by extrapolation, you can also use logistic regression analysis on the complete data till you get a clear picture. It requires a particular authority over data visualization tools to become a “data artist”. This is quite different from the UI we see on apps; a huge part of this is data about data or metadata. A very good example is Salesforce Metadata API that lets you access the website data from any location on the web.
v. A plethora of programming languages – the most commonly used programming languages that are still used by websites and servers include Java, Python, Scala and C. If you have your heart set on becoming a big data analyst after all, then you must at least have a working knowledge of these programming languages. As per a report by Wanted Analytics, there was an incredible 337% increase in the number of job postings for programmers who had a background in analytics!
Big data, analytics and data mining are all linked to each other. There is no way you can think about a career in big data without considering the roles of data mining and analytics in your daily work. 2017 is indeed the year for big data and big data analysts, but before jumping in weigh your skills and experience carefully.