682 tutorials · free to read
A working handbook for data and software engineering.
Hands-on tutorials by Alex Merced on Apache Iceberg, the data lakehouse, pipelines, agentic AI, and the languages and tools that hold it all together.
Browse by topic
All topics- Data Lakehouse201
- Data Engineering177
- Apache Iceberg142
- Dremio65
- AI Agents49
- developer tools33
- AI coding tools31
- agentic development31
- Databases28
- MCP25
- Data Architecture24
- AI23
- Apache Polaris22
- connectors21
Latest tutorials
Page 22 of 114- 01Autonomous Materialization for Agentic AnalyticsAutonomous materialization is useful when it is tied to workload evidence, governance checks, and lifecycle management.LakehouseJun 22, 2026
- 02Composable Semantic Layers for Analytical AgentsAI agents need more than metric names. They need composable business logic that survives multi-step analysis.LakehouseJun 22, 2026
- 03Built for Agents and Managed by AgentsDremio Agentic Lakehouse is easiest to understand as two ideas: data built for agent access and platform work managed by agents.LakehouseJun 22, 2026
- 04ClickHouse in the Loop for Active AgentsLow-latency analytical systems can help active agents, but only when event loops include validation, context, and safety boundaries.LakehouseJun 22, 2026
- 05The Context Layer for AI AgentsA semantic layer is necessary, but agents also need lineage, quality, freshness, compliance, and ownership context.LakehouseJun 22, 2026
- 06Lakehouse as the Operating Layer for Agentic AIAgentic AI announcements are useful when they validate the need for governed data, semantic context, and cost-aware execution.LakehouseJun 22, 2026