Think of the ingenuity behind self-driving cars, facial recognition, or automation. It’s the data scientist that you have to thank for that.
But a data scientist is only as good as the data they have access to.
And because data can be chaotic, disparate and messy, these scientists spend more time sifting through and cleaning data than actually innovating.
That’s where the data engineer comes in.
Data Scientists Need Clean Data
Data scientists have a few inescapable processes to go through before they can truly work their magic.
After identifying the problem that needs solving, they need to acquire, prepare and sift through data to do forecasts, deploy machine learning (ML) models and discover new areas to innovate in.
But getting access to clean, usable data is extremely time-consuming. Almost every company stores their data in a range of formats across different databases, with nothing standardised.
It makes the data scientist’s job even more painful to do.
What is a Data Engineer?
To make things a whole lot easier, data engineers rock up with their deep knowledge on how to build the pipelines needed to transform unusable data into clean, consumable data.
Data engineers are essentially the alchemists of the data science world. Just like turning lead into gold, they turn bad data into valuable, commoditised data.
They are as important as the data scientist, but prefer to work in the background, preparing the most suitable conditions for the scientists to thrive in.
Take the analogy of a high-level athlete versus a coach. The athlete takes joy in getting the acknowledgement and praise for the outcome that they produce. The coach, on the other hand, takes pleasure in experimenting, modifying and developing a powerful athlete with the tools available to them. Data engineers are like coaches in that they create the conditions for the athlete, or data scientist, to succeed.
What Do They Do?
Data engineers develop and maintain the data infrastructures and interfaces that businesses operate on.
Aside from setting the necessary processes in place to collect data from various sources, they also create systems that make the data usable for data scientists and analysts to innovate and form solutions.
With a monitored flow of healthy data running through the different systems and applications within a company, there’s a better chance for greater efficiency, productivity and major cost savings in virtually every area of a business.
They also design and then build out and install data systems that aid in the healthy function of machine learning and other AI capabilities.
All of this is necessary for the entire data science world to function effectively.
Looking for a Data Engineer?
Well, you’ve come to the right place.
For any business to be truly successful, it’s highly important to have the correct pipelines and foundations set in place.
Especially in a digital-centered age. The data that you have becomes essential to your progress, growth and innovation.
So if you’re in need of data engineers, we can help you.
If your data scientist is overburdened and limited, then we can turn that around for you.