The Future of Data Engineering in the Age of Generative AI

Data engineers have long been underappreciated champions of modern business. Many of the most brilliant successes of the digital age would not have been possible without the labor of workers toiling behind the scenes to build and manage the data pipelines, databases, and infrastructures that store and analyze the ever-growing rivers of information that characterize today’s competitive landscape.

But for the lowly data engineer, tomorrow is quickly approaching, and things are changing quickly. The emergence of generative AI has already had an impact on how we handle data on a daily basis. Engineering professionals can now focus their time and attention on tasks that are more valuable thanks to generative AI’s ability to automate a number of time-consuming but manual tasks.

Furthermore, these modest professionals are set to assume a new and crucial position in the commercial ecosystem due to the particular significance of data engineering to AI itself—they will no longer be unsung heroes but rather will be more important than ever.

Gen AI and the Data Engineer

The term “generative artificial intelligence” (gen AI) describes a new class of AI models that may produce fresh material based on the patterns and structures discovered from vast amounts of previously collected data. The most well-known example at the moment is OpenAI’s GPT-4, a natural language processing model that can generate writing that is fluid, coherent, and pertinent to its context based on user input.

The most obvious and immediate benefit of these technologies for data engineers is that they will let them create high-quality charts, graphs, and reports from data collection without (necessarily) soliciting the assistance of human designers or even analysts. Other generation AI models operate in the visual medium.

The primary objective of data engineering has always been to uncover the patterns and meanings concealed within a data set. Gen AI has the ability to not only aid in the identification of these trends and their implications but also to convey them in a manner that is so simple that non-technical brains can quickly understand them.

But data engineering’s “creativity” has never been restricted to charts. The work requiring the most imagination, abstraction, and “what-if” thought is the actual building of the data infrastructures.

Gen AI can also provide us with a tremendous boost in this area. As models advance, they will be able to manage these increasingly challenging data engineering tasks, such as feature engineering and schema building. By automating much of the technical tedium of data processing, such as coding or system maintenance, Gen AI is already releasing data engineering specialists to spend more time and inventiveness on valuable projects and more abstract thought.

The Data Side of Generative AI

Gen AI can produce new data in addition to potentially assisting data engineers in better managing the flow of current data. A company that is already overrun with data, such as one that is attempting to turn a “data swamp” into a more manageable “data lake,” for example, might not immediately see the benefit of this. But there are certain very important areas where new information can genuinely encourage advancement and aid in decision-making.

Data Augmentation: Every data engineer hates incomplete datasets, and generative AI models use cutting-edge machine learning techniques like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) to produce high-quality, realistic data samples. Just as GPT-4 can produce realistic, human-like text.

Anonymization of Data: Businesses must take steps to protect the privacy of sensitive user information in the era of strict data privacy laws like GDPR and CCPA. The statistical characteristics of the original data can be preserved in synthetic data created using generative AI models while no personally identifiable information is present. Then, without breaking privacy laws, this artificial data can be used for data analysis and other purposes.

Predictive Analytics: If future insights are as valuable to decision-makers as insights obtained from past and current company data, think of what they could accomplish with this information. While modern artificial intelligence (AI) does not truly possess the ability to foretell the future, it is capable of analyzing historical and current data in order to create well-informed forecasts about customer behavior, market dynamics, operational performance, and other important business elements.

An Unparalleled Connection

All of this serves merely to suggest that generation AI will have a tremendous impact on data engineers, changing not just how we work but also what our profession actually entails.

However, data engineering is unique in this sense because it is essentially the source of and the driving force behind generative AI. The sources of all their incredible power are the enormous training datasets that huge language models and their equivalents use, as well as the algorithms that sort, assess, and weigh that data into the billions—or perhaps trillions—of parameters that a model applies in order to produce new content.

In other words, data engineers are what software developers are to software or what mechanics are to vehicles when it comes to generative AI, and their importance will only increase. According to some projections, 60% of the training data for generation AI models will be artificial, produced by data engineers as a byproduct of gen AI, in less than a year.

Shortly put, the next few years are going to be an exciting journey for specialists who, in the public’s mind, are still largely responsible for creating pie charts out of the Q4 sales data from last year. As experts in all industries get acclimated to being the physical part of a human-machine contact, data engineers will increasingly act as matchmakers, chaperones, and couples counselors in these partnerships.

It’s not a stretch to claim that data engineers will have a direct impact on humanity’s future. On the other hand, the future of data engineering will be shaped by those who are most prepared and willing to harness the great power of this ground-breaking technology.

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