Insights & Tutorials

How Claude can use DataLAB's governed MCP tools to discover approved datasets, inspect schemas, run safe read-only analysis, and turn a useful result into a repeatable workflow.

How DataLAB uses SnapQL pipelines to turn one-off analysis into repeatable workflows for transformations, exports, validation, and model-driven analytics.

A step-by-step tutorial on training, evaluating, and comparing machine learning models using DataLAB's canonical SnapQL syntax - no Python required.

We built a desktop-first analytics product around SnapQL so teams can query data, run transformations, build pipelines, and train models without leaving the SQL surface.