3 Big Data Engineering Myths

The value of big data keeps growing. And for most organisations today, it’s essential that every byte of data is used effectively and with purpose.

But data is all over the place. It doesn’t all come in the same format. It’s difficult to work with and requires plenty of resources, time and specialised skill.

So data scientists are left to do the dirty work. 

While they’ve been hired to unlock real value out of that data, they’re often left to extract, clean, optimise and prepare it for proper use. It ends up wasting valuable time that could be better spent on innovation, insights and automation. 

That’s where the Data Engineer comes in. 

The Role of the Data Engineer

Data engineers are the people that make data accessible and workable for data scientists. 

They design and build out meticulous architectures for the healthy flow of big data.

Although they do work behind the scenes and aren’t as popularised as data scientists, they are equally as important for the functioning of any organisation. 

(More on what a Data Engineer is, this way…)

3 Common Data Engineering Myths 

Being that data science and the field of data engineering is still a relatively new field, there’s an inevitable abundance of myths and misconceptions. 

Let’s take a look at some of the most common ones:

Myth #3: Data Engineers are Glorified IT Specialists

A common mistake that people make is in thinking that data engineers are modern IT professionals. 

Computers are really the only common link between IT and data science. 

Apart from that, they both have very different roles to play in an organisation. The data engineer has a wide range of skills meant to design and develop data infrastructures that create fluid, transparent access to data throughout a business.

Data engineers also have extensive knowledge on coding in various languages and are able to build and maintain high-functioning software/application deployment and evaluation cycles.

All of it is very complex stuff that results in powerful innovations such as recommendation engines, automation, self-driving cars, and other-worldly user experiences.

It’s safe to say that IT professionals don’t have the knowledge and scope that data engineers have or require. 

Myth #2: Data Engineers Can Do Everything

While vast in their knowledge and practical abilities, data engineers can’t exactly handle everything.

Yes. There’s plenty that they are capable of doing for businesses of any shape and size, but as the objectives become greater, so does the need for more man (and women) power.

They’re usually adept at a variety of different areas of expertise. Think coding in different languages, building ETL systems, understanding cloud, DevOps and pipeline orchestration, and much more. 

But even they have limitations surrounding what they can do.

Data scientists, ML Engineers and UX Designers all have a pivotal role in making digital transformation work. Each of these disciplines require extensive knowledge and set the scene for full AI functionality. 

And unfortunately, data engineers can’t do it all. 

Myth #1: Modern Tools Might Replace Data Engineers

Indeed, many companies tend to use SaaS (subscription-as-a-service) tools to manage their data infrastructure.

But it’s the data engineers that operate and make effective use of those tools.

Building out a healthy, fluid and efficient data flow is one thing. Using the right tools to optimise and manage that data stack for performance is another ball game.

Both of which the data engineer is a pro at.

If anything, these modern tools improve and enhance the way that engineers do things.

It will end up creating more time and opportunity for data engineers to build, monitor, optimise and improve on important data pipelines.

Get Your Business Data-Ready Today

Data engineers aren’t exactly easy to come by. 

But we make it easier for you. 

We have world-class data engineers that have made a massive impact on many industry-leading enterprises.

If your business is in need of anything data, AI, ML, Cloud, UX related – feel free to contact us.

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Our DataOps Lead orchestrates quality Data Engineering best practices and expedites scalable data pipelines for rapid insight and the facilitation of machine learning and AI. His passion for Infrastructure as Code (IaC) through Terraform represents his inherent curiosity and enjoyment of solving real-world data problems.

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