The notebook / ideas in practice
Data Engineering.
Notes on data platforms, pipelines, and engineering decisions.
Pipelines, Part 2: Drafts, Audit Trails, and the Interfaces Around Them
Inside Datapipelines: how pipelines are versioned as drafts and releases, how agent changes are tracked, and how REST, SSE, MCP, dashboards, and published endpoints reach the same pipeline.
Cascading Dashboard Parameters, Resolved on the Server
Inside Datapipelines: how a server-side parameter engine resolves cascading filters, resets stale selections, and keeps dashboard client code reusable.
A Semantic Layer for AI Agents: Inside Datapipelines
How Datapipelines gives AI agents a semantic layer with evidence-backed facts, inline metadata, reusable business definitions, and explicit review boundaries.
Pipelines, Part 1: The Contract Before the Code
Inside Datapipelines: how we specified pipelines before writing code. The pipeline contract, its type system, REST, SSE and MCP interfaces, version history, and the planning discipline behind them.