Data engineering is set to be the profession of the future – here’s why
The centrality of artificial intelligence across sectors is glaringly obvious today – more obvious than ever before owing to the COVID-19 pandemic. This has, of course, had direct bearing on the landscape of skills required to stay relevant – and to carry industry forward. AI and Cloud computing have introduced possibilities for developers at a scale that was inconceivable even until a decade ago. Hence, the new task of today is to both engineer these developments and optimise the processes of analysis and inferencing. Enter: TheData Engineers!
The New-age Engineer
The engineering specialty has transformed drastically over the past few decades. Previously, before the age of the Cloud, data engineers and developers primarily managed production processes and scale within the software itself – there was no real dichotomy between software logic and hardware resources. Today, however, Cloud and elastic computing techniques have broken engineers into distinct sects to build software solutions, products and services and hardware development, management and services in order to truly reap the benefits of these elastic computing platforms.
Back-end Engineers: These are the people who will be involved in the building of the software logic and algorithms that is to be implemented. They will work with hardware development and look to optimise software based on computing power. This will (almost always) be projects that require massive scaling – especially when scaling involves much more complexity in tasks than a simple ‘if-then-else’ logic chain. The need for specialised expertise is, hence, a direct product of ever-evolving software complexities and computing power.
Front-end Engineers: These are the people involved in the topmost application layer and the UX/UI interface for the user. An engaging, logical and adaptable interface for Man-Machine interaction requires considerable dexterity and is a major part of the development process. Streamlining app development and production, as well as interfaces, however, still requires a major paradigm shift (that seems to be the offing).
DevOps Engineers: These are those individuals who ‘are responsible for scaling the software applet (the code container) onto the elastic Cloud for deployment so that it can effortlessly cater to as many users as are expected, and elegantly handle as much load as needed.’ Effectively acting as the route to the Cloud, these are people who must have in-depth knowledge of Cloud as well as software infrastructures.
Data at the Wheel
This paradigm shift in organisational structure is effectively going to be driven by one major cog: Data. Data is gradually shifting its role from being a cog in the wheel to being (almost) the wheel itself.
“Both Machine Learning and […] Deep Learning, are disciplines that leverage algorithms such as neural networks, which are, in turn, nourished by massive feeds of data to create and refine the logic for the core app.” These require the hand of data scientists, who design and train algorithms in order to execute system logic. This task, is, however, much more gruelling than it initially appears – because it is in the tuning, and retuning (and retuning) of this algorithm where the battle for the organisation is either won or lost.
The fact that almost all selection, optimisation and management of processes essentially rests on how clean data can be made right from the pipeline shows the importance organisations must place on data procurement and formatting. Machine/Deep learning algorithms require copious amounts of data reconfiguration to make it viable for usage and expansion. Hence, processing power and computational abilities need to be made very powerful in order to truly handle the test that is big data.
So, we turn back to data engineers once again.