Why is Data Engineering gaining popularity?

The modern world is so dependent on data that it is considered the new oil of the 21st century. As per a report by Business Insider, the planet generates 2.5 quintillion bytes of data across all domains and disciplines!

Data science is currently a booming industry. Data analytics insights is now an emerging trend which is expected to be valued at $1.2 billion by 2023. With an increasing demand for data engineers in the global market, you should consider pursuing this profession in the big data domain.

Although a relatively new discipline, data science has diversified into many sub-domains such as data analytics and data engineering. Opting to learn data analytics through big data engineering courses is a great place to start for success in this sector.

This blog talks about big data engineering, its benefits and why it has become such a popular route for many professionals in recent years.

What is data engineering?

Data engineering falls under one of the most basic new big data technologies. It’s all about solving analytical problems involving huge amounts of data. The process involves transforming the available data into a usable form for the data analysts to do their job.

You can think of data engineering as the process of breaking down complex food like carbohydrates (unusable data) into simpler compounds like sugars (usable data) before they can be digested by the body (data analysis). Data engineering also involves creating new programs and software that can break down complex data with greater speed and efficiency.

Data engineers are usually skilled in mathematics, computer programming, computer designing and machine learning techniques. This allows them to create data visualization techniques that optimize the flow of data through an organization’s database, essentially making things easier for data analysts and scientists.

What’s the current status of data engineering?

Previously, a lack of reliable data infrastructure was discouraging a lot of organizations to invest in a robust data analytics department, even if the business required it. In fact, the CTO of IBM reportedly said that 87% of data science projects in the company never saw the light of the day.

There were several reasons for these project failures. Firstly, companies were generating huge volumes of complicated data at a much higher frequency. This increased pressure on the existing data analytics team who didn’t have the resources to handle these vast amounts of data.

Secondly, most companies expected data scientists to organize this data and simplify it, which was outside the scope of their expertise. However, more and more companies gradually began to include data engineers within data analytics teams. This move lifted the workload for data scientists and analysts, allowing them to focus on their core responsibilities. It has also allowed companies to extract the optimal value from their data projects which has enhanced their annual revenue.

Owing to the growing importance of data analytics and data analytics trends in today’s technological and business domains, we have witnessed a huge surge in the number of data engineering jobs in the global market. The profession was included in LinkedIn’s report on the top emerging professions in 2020. Hired, a recruitment website, also reported a massive increase of 38% in data engineering jobs as compared to the last year.

If you are interested in a data engineering or data analytics career, you should consider a program that can provide real-life perspectives on the growth of data analytics and its challenges in the industry.

Toronto School of Management (TSoM) offers a Diploma in Data Analytics Co-op program that is developed in association with AWS Educate and Tableau. It includes a great co-op term experience which allows you to apply your academic knowledge in relevant industrial situations. The course has the added advantage of being qualified for Second Career Funding.

Click here to find out other course details of this diploma program at TSoM.