Clean, accessible data is the source of true innovation. It fuels insights, analytics, forecasts and even top-notch Netflix recommendations.
But it’s not like that type of data just simply exists. Most of the world’s data is messy, disparate and difficult to work with. Especially for data scientists.
So it’s truly up to data engineers to make the real magic happen.
Wait. Data Engineers?
Living under a rock, eh?
Let us enlighten you.
Data engineers are essentially the alchemists of the data science world. Just like turning lead into gold, they turn bad data into valuable, commoditizable data.
They are just as important as the data scientist, but prefer to work in the background, preparing the most suitable conditions for the scientists to thrive in.
This Is How They Make Innovation Work
Data engineers essentially build out data pipelines to prepare AI functionality.
These are the processes that help enable the smooth flow of data. Think data between a database and an application. Or a data warehouse and an analytics dashboard.
They essentially transfer raw data into readily accessible data for machine learning, AI and analytics systems.
Without the help of data engineers, data scientists end up wasting huge portions of their time. Most of it, lost to laborious tasks like extracting the data, cleaning it and then still building out the pipelines.
To make innovation work, data engineers set the stage for the scientists to perform and rake in the spotlight.
Data Warehouses and ETL
Since it’s the engineer’s job to make data readily accessible and available for the scientist, it’s important to understand how and where it will be stored.
One of the setbacks to working with raw data is that formats and locations for most data aren’t standardized. They come in all sorts of different shapes and forms.
That means all of the data needs to be consolidated in one suitable location for easier access and analysis. This ‘suitable’ location is usually referred to as a data warehouse.
To consolidate the data, engineers have to move data from an initial source to a new location. A process known as ETL (Extract, Transform, Load).
That is, reading or fetching a file from a particular source (extract), stripping and removing unnecessary information (transform), and then placing the data in a final warehouse for storage and access (load).
And this is only the beginning.
Creating Data Pipelines Is No Easy Task
Making all of this work also requires specialised knowledge of different coding languages, software platforms and other contemporary data-driven technology.
Data engineering is a powerful skill that takes advanced programming capabilities, software engineering skills and a deep understanding of data science as a whole.
Yes. It is possible for your data scientists to acquire these particular skills (if they don’t have them already).
But the problem lies in not using the skills that do differentiate them. They are capable of astonishing work, provided they have access to the right data.