Over the last decade, I've built highly scalable distributed data platforms and helped companies scale to processing multiple exabytes of data. My mission is to bring software practices followed by top tech companies to data engineering and help data engineers level up. I help data engineers land high paying tech jobs and significantly up skill themselves.
The 2 Principles Behind AI-Assisted Pipeline Development
|
Hello Reader, As data engineers, we are constantly bombarded with new AI terms such as skills, MCPs, agents, evals, etc. But it all comes down to 2 principles: Define & Refine. Define your workflow (i.e., how you’d approach a problem) as a Markdown file Refine it with additional information and make it more specific over time Learn: How to Use AI to Speed up Data Pipeline Development In this post, you will learn
Question for you: Do you feel unable to keep up with DE advances? If so, what is the main area (modeling, code design, AI dev, etc.) of your concern? Hit reply and let me know. I read every email. Best, Joseph |
Start Data Engineering
Over the last decade, I've built highly scalable distributed data platforms and helped companies scale to processing multiple exabytes of data. My mission is to bring software practices followed by top tech companies to data engineering and help data engineers level up. I help data engineers land high paying tech jobs and significantly up skill themselves.