5 Big Data Certifications That Are Presently in High Demand

by Guest Author on November 30, 2018

in Articles, Guest Posts

Big Data – it’s one of the most talked about aspects of IT today and people skilled in its disciplines are in particularly high demand.

Let’s briefly recap just what “Big Data” means and then move on to discuss some of the certifications you can obtain to help drive your career forward in this domain.

Knowledge

Lots of influential people in history have expressed sentiments running along the lines of “knowledge is power.”

Few would disagree, particularly when in the world of business, that power is typically equated with competitive advantage, etc.

However, there is a huge challenge here.  How does a company go about getting that knowledge?  It may have access to absolute mountains of data, but that’s different. There should be no confusion here – data is not knowledge.  Data needs to be transformed from a raw state into that knowledge base before it can be effectively drawn upon and deployed by businesses.

There are some significant challenges involved in doing so.  For example, capturing data at a low level of granularity means that the results can be almost incomprehensible in terms of providing big-picture views.  Equally, capture data at too high a level and a business will find it difficult to drill down and mine to a granular level if required. 

The end-to-end chain of capturing information from various sources in a suitable format, making cross-associations between elements of it and then transforming it into knowledge is broadly speaking, the domain of “Big Data” expertise.

Big Data Categories

Not all companies necessarily utilize a shared lexicon for describing how they see the breakdown and categorization of the generic subject called Big Data.  You may see different segmentation and terms used from one employer to another – so don’t be too surprised if you’re browsing vacant positions and see descriptions that don’t immediately ring a bell.

The good news is that there’s a degree of focus on three core concepts that underpin the idea of categories existing within this domain:

  • Structured Data

When analysing data, it’s commonplace to find that you are not starting at ground zero.  A lot of work might have already gone into building existing databases and structuring the data within them.  It’s widely cited that about 20%-25% of all total existing data comes into this category and is ready-made for exploitation by various information retrieval systems. These are sometimes referred to as “legacy systems.”

The sources for this type of data may be existing IT legacy systems or data entered by human beings.  A challenge in terms of the latter is often to try and verify human origin data and to cleanse it of errors and ambiguities.

  • Unstructured Data

If the idea of unstructured data residing with an IT environment sounds odd, it’s worth remembering that very substantial amounts of data originating from some types of technology systems and human being are in no discernible collective organized shape or form.  

Examples originating from technology systems might include things such as photographs, video imagery, word documents and the scanned images of manually created documents.  Similar examples originating from people might include texts, Emails, social media commentary and so on. 

By some estimates, unstructured data accounts for anywhere between 70-80% of all data stored electronically around the globe.  

  • Partly Structured Data

This is conceptually something of a grey area, as it relates theoretically to unstructured data, but which may have some structural components buried within it.  That might include things such as keywords, key phrases or references to individuals or companies, etc.

Yet again, partly structured data can originate from humans or other technology systems.  There is no real consensus as to what percentage of the total data stored comes into this category because it depends upon how you see the definitions of structured verses partly structured.  

This domain is particularly “hot” at present because it represents a huge and mostly untapped potential that might, in many cases, already exist within an enterprise.  This increasing recognition of partly structured data and its potential is one of the reasons behind the massive growth in things such as NoSQL as an interrogated and interpretive tool which does not rely on rigidly structured databases.  

Roles, Skills, and Certifications

There are several roles and disciplines with their associated skills which operate across all of the aforementioned categories.  

The big five in terms of demand and compensation include:

  • Cloud Architects – Amazon Web Services (AWS) and Microsoft Azure. These are the two main big data architectures in use across the Cloud and skills in both are considered highly desirable in the marketplace. This is a Master’s program that includes learning the architectural principles and services of these two top cloud platforms, then also how to design and build applications within them. Average salaries in the USA are running at between $90-$160k pa for these areas of expertise, indicating how much employers need and value these skills.    
  • Data Scientist.  The Big Data domain is based on the concept of data and its exploitation. As a result, Data Scientists are in huge demand. This Data Scientist Master’s program covers no less than 30+ tools and skills that are in high demand including SAS, R, Big Data Hadoop and Spark, Data Science with Python, Business Analytics with Excel and Apache Kafka. Salaries for certified Data Scientists in the US currently have averages in the range $110-$175k pa.     
  • MongoDB Developer and Administrator Certification Training.  As was outlined under the types of Big Data section, most data held in society today is unstructured data. There is a huge emphasis on exploiting that data and MongoDB and associated NoSQL techniques are becoming hugely popular. The 32 hours of instructor-led certification training covers NoSQL plus data modeling, ingestion, query, sharding, and data replication.  Average salaries for an experienced MongoDB professional range between $90-$180k.  
  • Integrated Program in Big Data and Data Science. This is a Master’s program that is the foundation stone for building in-depth knowledge across multiple data-driven domains. It provides 200+ hours of instructor-led training covering Data Science with R, Big Data Hadoop and Spark Development, Tableau Desktop Associate Training, Data Science with Python and Machine Learning. Salaries for professionals with integrated Big Data and Data Science experience are currently in the range $100-$220k pa.
  • Big Data Hadoop Architect. This Master’s program will turn you into a qualified Hadoop Architect. The study covers over 50 highly sought-after tools and techniques including Spark and NoSQL database technology plus other Big Data approaches including Storm, Kafka, and Impala. Certified Hadoop architects can command salaries of up to $200k and are generally amongst the highest-paid people in IT.  

Career Advantages

In today’s marketplace, employers recognize that business success is driven less by what they do operationally and more by what they know. This is part of the transformation of much of our economy towards a knowledge-based foundation.   

Transforming raw data into business Intelligence is something that can make the difference between success and failure for many enterprises. It’s for this reason that the market is so ripe for career development for individuals with appropriate data-related skill sets. That’s because what you know and can be shown to know is what makes you highly valuable and marketable.  

The original emphasis on big structured databases has now been considerably softened by the realization that data exists in many different shapes and forms and on many different electronic media.  Enterprises that have large legacy systems need to be able to access and exploit the data there.  Those that do not but are building from a green-field perspective will want to make sure that their data strategies and architectures are optimally positioned at the outset to support future exploitation.

For all these reasons, these various career paths are some of the most promising within IT at present.

How We Can Help

At Simplilearn we have a range of courses and career path progression options that have been structured with career progression and development in mind.

Choosing the right courses and pathway to certification is essential if you are to maximize your potential and leverage that to achieve greater career success.

Why not contact us for a discussion at your earliest opportunity to see how we can work in partnership with you to help you achieve your career ambitions.  

Guest article written by: Raghavarao works as a Data Analyst. When not doing anything related to Analytics, he goes around India. He regularly writes about hadoop, data science, artificial intelligence on many niche blogs, he also blogs on  lot of other things on his blog iamdoingseo.

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{ 1 comment… read it below or add one }

Tarique Amir December 1, 2018 at 09:21

Hi Raghavarao,

Good post with very helpful information. These certifications is quite important and helpful in today’s high demand business scenario.

Thanks for sharing, have a good day.

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