The AI-fication of Data Engineering Bolsters AI Development


For AI to produce high-caliber output, be it analytics insights or completely new content, AI needs well-sourced, standardized, and well-governed data. In short, one of the primary things that AI needs is data engineering. Yet as of this writing, AI has advanced so much as to be able to help fulfill data engineering tasks. A new slate of state-of-the-art AI tools can now take a significant amount of data engineering workloads from the hands of data engineers in a transformative process we call “AI-fication.”

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Figure 1. A visualization of today’s tools, platforms, and technologies that can utilize AI in an end-to-end data ecosystem  

Thanks to the power provided by AI, IT leaders can better address data management problems, such as:

  • Filling the demand for data engineers.
  • Filling the roles of data managers and stewards while they’re away.
  • Preventing the loss of productivity that would have occurred if too many people were involved in handoffs done across the value chain.
  • Generating high-quality data that would normally take a lot of time and manual effort from a large data engineering team.

Through the AI-fication of data engineering processes, data engineers are freed up to do other tasks, such as provide their insights to business decision-makers. Given the engineers’ intimate knowledge of business data, they are likely to have ideas pertaining to the business that could be tapped if only they were free to be tapped. Additionally, AI tools empower data developers and engineers who lack deep expertise to accomplish advanced tasks. For example, data transformation engine users can talk to AI assistants using natural language to ask for help on writing queries in SQL or other language that the engine requires.

Beyond reducing data engineers’ workloads and expanding their value creation capabilities, the AI-fication of data engineering ultimately means that AI augments itself. It is like having robots enhance a robot-building machine so that it produces a much better robot. And for enterprises, having the better AI is to outcompete others in the business arms race. A better AI could mean attaining more cost-efficiencies, more accurate predictive capabilities, better management of systems that keep growing in complexity.

Download our white paper, “The Present and Future Value of Data Engineering to Enterprises,” to learn:

  • How AI assistants can help fledgling data developers initiate data transformation steps like ingestion and processing
  • How AI-aided data catalogs grants benefits to enterprises, such as helping to optimize its data governance and business efficiency.
  • How data observability tools help data engineers construct, test, and maintain architectures, such as databases.
  • How unified data platforms like Databricks Lakehouse empowers enterprises to create their very own generative AI solutions more quickly.

As of this moment, too many companies can only say that they have AI but are unable to unleash its capabilities. Now, imagine leapfrogging away from them all, thanks to your superior technological prowess. You’ll be able to do this because you’ll not just understand the benefits of AI-powered data engineering, but also because you understand the pitfalls of letting your enterprise be left behind.

Evolution of Data Engineering with AI

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