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Data Analyst versus DevOps Engineer: A Comparative Analysis on the Emerging Job Market Trends

For a majority of the graduate Computer Science and IT engineers passing out from the top BE and B Tech colleges in India, the ultimate goal in their career for them is to lead a data science company in the near future. The popularity of data science courses in India has exceeded all kinds of expectations and the recent trends showcase that the juggernaut isn’t going to stop for data science professionals in the near future. What truly matters in this industry as far as success and reputation are concerned is talent and skill management. We asked some of the leading industry experts in the data science industry who closely associate themselves with the best institute for data science about the core engineering skills required to gain mastery over data science subjects. We were pleasantly shocked to learn that a majority of the data scientists we spoke to have come from very modest and humble backgrounds of analysts and developers. When we analyzed the current job and salary trends in the data science industry, we found out that data science companies prefer to hire professionals who have the highest education qualifications bundled with superior programming skills in popular and emerging coding domains such as JAVA, C/ C++, Python, R, MATLAB, and others. In fact, Python and R programmers were found to be the fastest movers in the ladder of hierarchy in the current data science ecosystem. 

So, we are forced to ask this question during admission processes to the best institute for data science  – “In data analyst versus DevOps engineer battle, who is going to emerge as a winner?”

Let’s equate the interesting relationship data analysts share with DevOps.

#1 the beginning and the journey

A data analytics is a specialist personnel entrusted with duties to glean through data lakes and to come up with meaningful and actionable insights using the contextual data for better decision making. A data analyst has to demonstrate superior coding and programming skills that can help the data science teams to identify usable data from first party and third party data sources. It is ultimately in a DA’s zone to develop databases and data management systems that could be further restructured to perform data analysis using filters, performance analysis tools, and so on. In the value chain of data engineering, data analysts have to demonstrate their experience and knowledge in the various programming languages such as SQL, Apache Hadoop, R, MATLAB, Python, and so on. They should have a proficient expertise in handling various business intelligence tools such as SAS, Tableau, and Qlik. 

On the other hand, a DevOps engineer is more focused on building the IT side of the data science infrastructure. The primary role of the DevOps engineer is to come up with solutions at various stages of the DevOps life cycle which involves Coding, Building, Testing, Modelling, Deploying, Moderating, Operating, Monitoring, and Implementing / redesigning. Compared to a data analyst, the workflow of a DevOps engineer is more complex and involves consistent engagement with IT operations influencing the data management lifecycle. These are established by virtue of the Continuous Improvement / Continuous Development, ETL / ELT, and Virtualization of the data management operations, also called Data Ops in the current context of doing business with data in the IT ecosystem where Clouds, Virtual Machines and Containers are deployed.

If you closely compare the roles and titles these two specialists garner in their lifetime, you will find that data science is an intrinsic bloodline connecting the two. Yet, the distinction in their actual workflows and processes renders them disjointed at the organizational level due to IT gaps and collaboration limitations. In larger companies, a Project Manager or a data scientist could be working with the Chief Information Officer (CIO) or Head of IT and Data Officer to reduce these gaps, but in reality, this is still shaking the grounds. 

#2IT Infrastructure networking and data ops provisioning

When we think of data science domains, we restrict ourselves to merely focusing on the analytical side of the business. The real force actually arrives from the work that the DevOps engineers’ teams who provided the use of AI and machine learning capabilities within the data life cycle where data analysts are the end users. To ensure that the data science teams run smoothly and in an agile fashion, DevOps engineers would implement various AI and Machine learning tools and solutions to automate a bulk of computational programs which can be easily trained to analysts in a data science course online. These tools and solutions exemplify the performance of programming language based platforms running on Juno, NumPy, and others. Complex architecture running on AWS Cloud, Microsoft Azure, and Google Cloud is also handled by the DevOps teams to enable the delivery of cloud-based analytical tools to BI teams.

#3 Data and its impression on the DA versus DevOps engineer outcomes

For an analyst, business understanding of how data impacts the organizational goals in the short and long term is very important. Without acquiring a first hand knowledge of data points impacting the business decisions, data analysts can seldom rise to the top. In short, it is OK for a DA to fall short of coding skills, but missing out on data analysis principles can cost the career big time. For some, this limitation could result in a dead end. On the other hand, there is no option for DevOps engineers except for mastering every bit of data – right from how to prepare data to distributing this data for exploratory analysis as applied to data visualization tools that eventually appear on the dashboards of their data analyst peers in the company. 

So, if data visualization is the goal of the company, it should be understood that DevOps Engineers would deploy the data models that produce graphical visualization strategies for data analysts. For Data science companies, data analysts serve as the internal customers to DevOps engineering teams.

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