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!
Technology Explorations in Data & AI
Claim This Podcastby Dataminded
15 episodes
Updated Daily
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Podcast Overview
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!
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Publishing Since
10/13/2025
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Recent Episodes
May 19, 2026
Metric Views in Databricks: The Missing Layer for AI Agents
<p>AI is only as good as the layer underneath it. That layer in Databricks is called a Metric View: a semantic model that defines your measures, your joins, and the rules for how data fits together. </p><p>In this episode, Stefan walks through the full stack end-to-end on a real LEGO dataset: building Metric Views in Databricks, generating them with dbt, deploying through Databricks Asset Bundles, and finally exposing it all through a Genie Space that business users can chat with. Along the way we get into some deeper questions: what the limits of metric views look like, how access control works, what BI "shifting left" actually means for engineering teams, and how you organize this in data products.</p><p>If you saw our Snowflake Intelligence episode, this is the Databricks counterpart. The Snowflake episode can be found here: https://www.youtube.com/watch?v=Gp-BntPgpcU</p><p>Resources:<br>- Demo code: https://github.com/datamindedbe/demo-technology-exploration<br>- [episode-specific links here]</p><p><strong>Creators & Guests</strong> </p><ul> <li><a href="https://techex-data-ai.transistor.fm/people/jonny-daenen">Jonny Daenen</a> - Host</li> <li><a href="https://techex-data-ai.transistor.fm/people/stefan-van-raemdonck">Stefan Van Raemdonck</a> - Guest</li> </ul><p>---</p><p><a href="https://www.youtube.com/watch?v=eP7lnTXiX-w" title="Click here to watch a video of this episode.">Click here to watch a video of this episode.</a><br> Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb</p><p></p><ul><li>(00:00) - Chapter 1</li> <li>(00:00) - Intro </li> <li>(00:50) - Meet Stefan </li> <li>(01:43) - Demo: talking to your LEGO data </li> <li>(07:17) - Why semantic layers? Data products explained </li> <li>(11:20) - What's in a metric view? </li> <li>(14:15) - Creating Metric Views in Databricks </li> <li>(16:29) - The LEGO data model </li> <li>(18:17) - Data Products & Metric Views in Databricks </li> <li>(22:07) - Are we limited to predefined measures? </li> <li>(23:16) - Building: native Databricks SQL bundles </li> <li>(29:12) - Building: dbt </li> <li>(31:16) - Maintenance and BI shifting left </li> <li>(33:59) - Deploying dbt on Databricks </li> <li>(35:29) - Creating the Genie Space </li> <li>(38:24) - Access Control and Permissions in Data Queries </li> <li>(39:28) - Writing SQL on top of Metric Views </li> <li>(40:10) - Takeaways and favorite Lego </li> </ul><br>---<p>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.</p><p>Music by Aleksandr Karabanov from Pixabay</p>
May 7, 2026
Snowflake Intelligence: The End of Dashboards?
<p>Your dashboards only answer the questions you thought of last quarter. Every new question is a ticket, a dependency, or a gut call. Snowflake Intelligence wants to fix that -- a chat interface on top of governed enterprise data that turns plain English into SQL, runs it, and gives you a chart back. No analysts involved.</p><p>Jelle builds the full setup live: semantic view, verified queries, Cortex Agent, access control. They get honest about what this actually requires -- data quality, governance, and whether Snowflake is worth the cost.</p><p>Resources:<br>- Snowflake Intelligence docs: https://docs.snowflake.com/en/user-guide/snowflake-intelligence<br>- Demo code: https://github.com/datamindedbe/demo-technology-exploration<br><strong>Creators & Guests</strong> </p><ul> <li><a href="https://techex-data-ai.transistor.fm/people/jonny-daenen">Jonny Daenen</a> - Host</li> <li><a href="https://techex-data-ai.transistor.fm/people/jelle-de-vleminck">Jelle De Vleminck</a> - Guest</li> </ul><br>---<p><a href="https://www.youtube.com/watch?v=Gp-BntPgpcU" title="Click here to watch a video of this episode.">Click here to watch a video of this episode.</a><br> Full playlist: https://www.youtube.com/playlist?list=PLJ_da7qdfL80rA7byzC_CmyrfJWjcCTnb</p><p></p><ul><li>(00:00) - Intro & meet Jelle </li> <li>(02:04) - What is Snowflake Intelligence? </li> <li>(02:54) - Demo: talking to your data </li> <li>(10:36) - Where it fits & how it works </li> <li>(15:14) - Build: the Semantic View </li> <li>(22:37) - Build: the Agent </li> <li>(29:20) - Challenges & data quality </li> <li>(34:26) - How ETL is evolving </li> <li>(40:33) - Will this replace engineers & analysts? </li> <li>(43:23) - Is Snowflake worth the cost? </li> <li>(45:14) - How do you get started? </li> <li>(50:06) - Summary & takeaways</li> </ul><br>---<p>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.</p><p>Music by Aleksandr Karabanov from Pixabay</p>
April 23, 2026
AI Workflows in Agno: Building Deterministic Agents
<p>Enterprise data is full of sensitive information: different teams, different access rights, different rules. When you ask an AI agent a simple question and get "access denied," it's not a permissions bug. It's a design problem.</p><p>Pascal has been exploring how to tackle this using Agno, an agent framework built around deterministic workflows. Instead of letting a single agent roam freely across your data, Agno lets you build specialized agents, each with its own access rules and instructions. Workflows orchestrate these agents with guardrails that keep humans in the loop when it matters.</p><p>In this episode, Pascal Knapen, CTO at Dataminded, demos the full flow: from a natural language question, through an access check, to a verified answer. We explore how skills differ from workflows, how Agno handles dynamic agent creation and deployment, and how LLM-based evaluations act as a quality judge for agent responses.</p><p>Additional Resources:</p><ul><li>Demo code: https://github.com/datamindedbe/demo-technology-exploration/tree/main/demos/agno-workflows</li></ul><p><br>Intro music by Aleksandr Karabanov from Pixabay</p> <ul><li>(00:00) - Intro: AI agents and enterprise data </li> <li>(01:50) - Two ways to give AI access to your data </li> <li>(03:23) - Skills vs Workflows </li> <li>(04:45) - Demo: AI with controlled data access </li> <li>(07:45) - The workflow is deterministic - the agents aren't </li> <li>(09:13) - Demo: evaluations & reliability </li> <li>(11:00) - Code walkthrough with Agno </li> <li>(17:45) - Why Agno? An honest take </li> <li>(19:46) - Identity & exposing as an API </li> <li>(20:24) - Takeaways </li> </ul>
15 total episodes available
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