AI Mastery for Data Engineers: Advanced Research Techniques to Go Beyond Basics

Equip yourself with high-level research methods to choose the best tools and design scalable architectures.

Check my AI for Data Engineers playlist to read more about using AI in Data Engineering.

Greetings, curious reader,

There's no shortage of articles and tutorials on prompt engineering and basic AI concepts. But as a data engineer, you probably feel like all these resources scratch the surface and are AI-generated themselves.

How do you put AI to work in data engineering? How do you find the right tools, design scalable architectures, and implement practical solutions in a real-world environment?

In this series, you and I are diving deep. We won't cover the basics of prompt engineering or elementary AI concepts — there are plenty of generic resources online.

Instead, I'll focus on what data engineers need to integrate AI effectively. This week, I will show you who to:

• Find the best tools for specific tasks.

• Learning from how big companies design their data architecture.

• Creating your own robust, scalable data architecture.

I have two sets of prompts today:

• Basic for everyone

• Advanced for Pro Data Gibberish members.

But here's the thing: Basic prompts are good enough. You can do a decent job even without supporting my work.

Wanna see the advanced Library? Check this page.

Last but not least. I am using Perplexity for this week's article. But they do not sponsor this article. They don't even know I exist. There are no affiliate links whatsoever. I genuinely use (and pay for) Perplexity.

I'm very excited. Let's get started!

Reading time: 10 minutes

Research Level #1: Researching and Selecting the Right Tools