Data Engineering Podcast
This show goes behind the scenes for the tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation are just some of the topics that you will find here.
Latest Episode
The AI Data Paradox: High Trust in Models, Low Trust in Data (10.11.2025)
Previous Episodes
- Bridging the AI–Data Gap: Collect, Curate, Serve
- Beyond the Perimeter: Practical Patterns for Fine‑Grained Data Access
- The True Costs of Legacy Systems: Technical Debt, Risk, and Exit Strategies
- Context Engineering as a Discipline: Building Governed AI Analytics
- The Data Model That Captures Your Business: Metric Trees Explained
- From GPUs-as-a-Service to Workloads-as-a-Service: Flex AI’s Path to High-Utilization AI Infra
- From RAG to Relational: How Agentic Patterns Are Reshaping Data Architecture
- Duck Lake: Simplifying the Lakehouse Ecosystem
- Aligning Business and Data: The Essential Role of Data Modeling
- From Academia to Industry: Bridging Data Engineering Challenges
- High Performance And Low Overhead Graphs With KuzuDB
- Bridging Data and Decision-Making: AI's Role in Modern Analytics
- From Bits to Tables: The Evolution of S3 Storage
- Revolutionizing Python Notebooks with Marimo
- Warehouse Native Incremental Data Processing With Dynamic Tables And Delayed View Semantics
- Streamlining Data Pipelines with MCP Servers and Vector Engines
- Foundational Data Engineering At Two Sigma
- Enabling Agents In The Enterprise With A Platform Approach
- Dagster's New Era: Modularizing Data Transformation in the Age of AI
- AI and the Lakehouse: How Starburst is Pioneering New Workflows
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