The big data analytics industry is well known for its everlasting shortage of skilled personnel. We had Gartner predicting in 2015 that there will be a shortage of 200,000 data analytics professionals in USA by 2020 and we still have the McKinsey project declare a potential shortage of 190,000 data professionals in USA by the end of 2018 itself. Time has passed but the predictions have not really changed. This is true of the whole world and of course of Malaysia. Big data analytics industry has skill shortage which implies, it packs great opportunities for those who are ready to embrace it. It is a good time really to get your big data certification.
What skills should you focus on then?
Big data has come to be a large, all inclusive field. The term big data was supposed to refer to the large amounts of data which was difficult to manage with traditional tools and methods. But currently big data vaguely encompasses all the tools and techniques used to manage and manipulate the large amounts of data. Understandably it is not a single solid skill set but a whole stack of different skills which help a professional to act as a big data analyst. These skills gain and lose importance, supplanting a previous skill and being supplanted by a new technique. Some however stand the test of time.
You don’t want to miss Hadoop
Apache Hadoop had come to be synonymous with big data due to its monopolistic usage for data storage, processing and analysis. Times have changed and new tools are making their way into the industry. But there is a lot of companies which have high stakes running in the Hadoop framework, so, the importance of Hadoop is going nowhere for the time being.
HDFS, Mapreduce, Flume, Pig, Hive, Hbase, YARN, etc. are some tools in the Hadoop ecosystem. HDFS among this is the most indispensable due to cost-effective data storage. Yes, cloud storage is there but distributed file storage is still relied upon by many.
Light it up with Spark
Another tool owned by Apache, Spark has technically ousted Mapreduce. Spark is fast, efficient, real- time and cost-effective. It can process data within or without a Hadoop eco-system. Learn Spark and you have an advantage over your peers.
R, SAS and other quantitative analysis skills
Both R and SAS can be used for data mining, data processing and building analytical models. These tools have been around for quite some time and professionals skilled with these are definitely in demand. Add Python skills to that and you become an asset to any company.
Among other things you can devote some time for learning NoSQL skills because that is in demand too. If you are dealing well with big data analytics, do not shy away from taking some training in machine learning. It is a different field but think how deeply they are connected; it is only with well chosen data that you can train a machine learning algorithm. Get your big data certification , and do not stop learning; you will be pretty much invincible.