Home » Business Topics » Data Trends

Big data engineering – A complete blend of big data analytics and data science

  • Aileen Scott 
business man hand working on laptop computer with digital layer
business man hand working on laptop computer with digital layer business strategy and social media diagram on wooden desk

Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn.


Globally several individuals have chosen data science and big data engineering as a mainstream career option, but still there are people who are unaware of about the various options that are available. Many claims suggest big data analyst will be discarded after big data, while a few claim that big data and data science are similar, or one is a subset of another.

Data science has been here for a long time, while big data, on the other side, and is fairly new, originating from the former with many relevant transformations. Big data analytics leverages the techniques and software systems that are helpful in either and vice versa with respect to techniques.

But the role of data engineer is as much important as the data scientists is. Because if data scientist develops a breakthrough algorithm, then the big data engineer puts it into production for the use by the business, according to a statistics report from Gartner that only 15 percent of big data projects ever make it into production. And while many never seek into the reasons why 85 percent of the big data projects never make it.

Let’s have a look at the comparison between data scientist, big data analyst & data engineer


Big data engineering is the process of developing and building systems for collecting, storing, and analyzing data. It is a wide field with various applications in several industries. Firms have collected massive amounts of data, and they need data infrastructure and personnel to sort and analyze the information. This resulted in the demand for professional data engineers who work to design systems that collect, manage, and convert the raw data into usable information. 

This information is useful for data scientists and business analysts to interpret. The main objective is to make data accessible so that the companies can take help of it for evaluating and optimizing their business’s overall performance. That is why for every one data scientist firms require at least two data engineers. It can be said that, one may needs as many as 5 data engineers per every 1 data scientist.   

A data engineer is the all-purpose everyman of a big data analyst operation, working between downstream analysts on the one hand, and upstream data scientists on the other. They will often come from programming backgrounds and are experts in Big Data frameworks, such as Hadoop. They’re called on to ensure that data pipelines are scalable, repeatable, and secure, and can serve multiple constituents in the enterprise. 

Now, that little surge in demand seems to be blossoming into a full-blown data engineering shortage. According to a new report released by Stitch and Galvanize, there are only 6,500 self-reported data engineers according to an analysis of their LinkedIn profiles, but more than 6,600 job openings for data engineers in the San Francisco Bay Area alone.