
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!
| Publishes | Weekly | Episodes | 15 | Founded | 5 months ago |
|---|---|---|---|---|---|
| Categories | EducationTechnologyHow To | ||||

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.
In this episode, Stefan walks through the full stack... more
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 Engli... more
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.
Pascal has been explo... more
Jan opens 12 browser tabs every morning to check overnight pipelines. Log in, check, close. Twelve times. So he built a terminal app instead.
Flowrs is a TUI for Apache Airflow written in Rust. Navigate all your environments from the keyboard, drill... more
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Tech-focused unscripted conversations featuring practical demos, real-world tooling, and hands-on walkthroughs in data engineering and AI. The show centers on building AI agents, RAG workflows, cost optimization in cloud environments, and data pipeline orchestration, with frequent live demos, code examples, and open-source tooling. Guests range from CTOs and senior engineers to subject-matter experts from Dataminded, Convair, MindsDB, and related ecosystems, offering actionable guidance and trade-offs in implementing scalable data and AI solutions. A notable strength is its emphasis on usable, reproducible demos and cost-conscious engineering practices, helping practitioners cut through hype to assess what actually works at scale. This set ... more
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