Will Data Engineering be replaced by AI? No — Then Why?

By 2024, artificial intelligence will make data engineering harder. According to our survey, 50% of data managers claim their supervisors pressure them to increase AI spending, even at the expense of reducing expenditures on higher-paying initiatives.

The pressure is on data engineering teams to upgrade their systems, acquire updated skills, master sophisticated AI, improve large language models (LLMs), and address all the ethical, security, and privacy concerns associated with utilising AI. 

Many people are curious about AI, including how far it will go if it "takes over" and what this implies for the current systems and workflows. Though AI will continue to develop, it can only partially replace conventional data engineering methods.

Why AI won't replace data engineering 

AI cannot replace data engineering and other essential roles. Please feel free to skip this section if you are uncomfortable delving into such profound thoughts. 

Are you still with me? 

Start reading, How AI and Data Engineering works together?

Limited artificial "intelligence" 

It doesn't make anything "intelligent" because it can write proper code or speak like a human. The ability to do these tasks implies intelligence. It's not very intelligent, but it could be helpful. 

Intelligence goes beyond responding to a query. Intelligence entails creativity and knowledge comprehension. No matter how much data we give an AI, all it can do is regurgitate back what it has learned, albeit highly sophisticatedly. 

AI is not capable of abstract thought like a data engineer. In reality, it doesn't "think" at all. AI does the tasks we give it. True capability extends much beyond just following directions. 

AI needs to comprehend business. 

A thorough grasp of the company's issues and requirements is fundamental to data engineering and AI. This entails conversing with your organisation's individuals and paying close attention to their challenges. It also involves determining what they need—which may vary from what they claim—and then developing a data solution that offers genuine value in response to those requirements. 

Indeed, given that you have all the facts and know what to do, AI may speed up the process. However, it's crucial to remember that AI is only a tool that supports automation or creates systems using your laborious work. It would help if you put in the time and effort to comprehend the issues at hand. 

AI cannot comprehend and apply responses in context. 

AI is now configured to generate specific results. However, a data team is still needed. This requires a lot of context, such as understanding who will use the code. It also involves understanding who verifies its appropriateness for a given application. It also requires understanding who knows how it will affect the system and data pipeline structure. 

Although writing code is helpful, data engineering and AI involves much more intricate and abstract work. This position requires critical thinking, problem-solving, comprehension of the interplay between various components, and identifying opportunities for generating value for the company via multiple initiatives. AI still needs to be able to think and solve problems with this degree of creativity. 

Data engineering with AI

Data engineering allows artificial intelligence to develop and maintain applications. Data engineers increasingly take on increased responsibility for integrating AI into enterprises and building and maintaining the infrastructure supporting the data stack. The advanced competencies discussed earlier—such as abstract thought, business comprehension, and context-driven problem solving—are essential for designing and overseeing AI systems. 

This implies that in the event of a malfunction, a "human-in-the-loop," as it were, must supervise the data engineering with AI and identify any problems. 

And what powers all of this data engineering with AI? If you're doing it well, it's a lot of your original data. Data engineering in the age of  AI can solve many simple problems and may serve as a foundation for solving more complicated ones. However, this is only possible if someone promptly feeds the system with accurate data and guarantees high-quality data. 

What AI is likely to do? 

Artificial intelligence (AI) is crucial in data engineering, and many people think it improves team productivity. 

Managing large amounts of data with large language models (LLMs) would transform fundamental engineering skills. We're beginning to employ AI to create code for us as humans move from writing all their code by hand to utilising pre-written code packages. 

Data engineers spend much time debugging programs or extracting information from large databases. Data engineering in the age of AI can do these activities faster by generating simple code and evaluating large volumes of data. 

AI is valid for: 

Automatically identifying matches in disparate data sources and generating the necessary code to connect them. Error detection and correction, alerting engineers of more significant issues, and organising disorganised data for updated system usage, such as overseeing routine data management chores. Due to AI taking care of these tedious tasks, data engineers will have more time to work on larger, more valuable projects. 

How to prevent AI replacement in Data Engineering?

When it comes to data engineering, it's crucial to remember that if your skills are limited to simple jobs, you should not worry. 

Regardless of our position—data engineering, analyst, CTO, or CDO—we must ask ourselves, "Are we creating added value?" 

If not, it's time to improve what we do. 

To make sure you're offering value that AI can't match, follow these steps: 

• Get to know your industry: AI cannot understand business situations. Learn about data usage and build positive connections within your organisation. Your ability to provide data solutions that benefit your stakeholders will prove more valuable as you gain a deeper understanding of their needs. 
• As AI automates more data engineering jobs, data teams risk being seen merely as a financial burden rather than an asset. Leaders must show how valuable their teams are to the firm. Although challenging, resources such as the data ROI pyramid may help. 
• AI cannot function without high-quality data. The ability to identify and resolve data problems must be a strong suit for data engineers. Using appropriate resources and techniques is the key to ensuring the data you use for your AI projects is accurate and reliable. 

So, knowing and understanding the AI and Data Engineering, Are you up to Data Engineering Services, if Yes, then Contact us for free consultation.