Technology Explorations in Data & AI

Emil Krause & Jonny Daenen explore how to accelerate dbt development by integrating MCP (Model Context Protocol) with Postgres and Cursor. Emil demonstrates how to solve a database bug by allowing AI agents to interact directly with databases. 

They discuss the setup of a database MCP server, demonstrate its capabilities in troubleshooting data inconsistencies, and highlight the importance of understanding data even when using advanced tools. The conversation also touches on the potential pitfalls of using such tools and the need for technical expertise in leveraging them effectively.

Resources:
  • Demo code: https://github.com/datamindedbe/demo-technology-exploration/tree/main/demos/postgres_mcp
  • MCP 101: https://www.youtube.com/watch?v=fIr55-koOJQ
  • Click here to watch a video of this episode.
  • Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb

Chapters:
  • (00:00) - Introduction: MCP + Postgres
  • (02:20) - Demo: debugging salary percentiles
  • (06:29) - Creating and testing dbt models
  • (07:11) - Benefits and dangers of AI assistance
  • (09:42) - Setting up Postgres MCP in Cursor
  • (12:57) - Challenges & pitfalls
  • (14:54) - MCP vs semantic models
  • (17:16) - Other dev tasks
  • (18:39) - Claude Desktop vs Cursor
  • (19:59) - Summary

Data & AI: Technology Explorations is a biweekly show from Dataminded. Each episode a Dataminded engineer demos a tool or technique worth knowing about -- working code, honest takes, no hype.

Music by Aleksandr Karabanov from Pixabay

Creators and Guests

Host
Jonny Daenen
Head of Knowledge at Dataminded
Guest
Emil Krause
Data Engineer at Dataminded

What is Technology Explorations in Data & AI?

Deep dives and practical demos on the technologies shaping modern data and AI development. Join the Dataminded team as we explore, unbox, and critically review the latest tools, from building AI agents and RAG systems to optimizing cloud costs and accelerating data pipelines. We cut through the hype to show you what actually works in real data engineering practice, complete with demo code!

00:00 Introduction: MCP + Postgres
02:20 Demo: debugging salary percentiles
06:29 Creating and testing dbt models
07:11 Benefits and dangers of AI assistance
09:42 Setting up Postgres MCP in Cursor
12:57 Challenges & pitfalls
14:54 MCP vs semantic models
17:16 Other dev tasks
18:39 Claude Desktop vs Cursor
19:59 Summary