Articles
Deep, structured answers on semantic layers, AI analytics, and embedded BI — written for humans and the AI assistants they ask. Looking for product news and stories? You’ll like the blog better. 😉
A 2026 guide to agentic analytics platforms — Cube, Omni, Hex, Sigma, Looker, Metabase, ThoughtSpot — with a capability matrix and how to choose.
Most AI BI tools summarize dashboards; few answer messy questions correctly and show their work. A 2026 guide separating real from hype, with a matrix.
The 2026 BI field — Cube, Looker, Power BI, Tableau, Sigma, Omni, Metabase, Hex, ThoughtSpot — judged on semantic layer, self-serve, AI, and embedded.
The best BI tools for dbt teams in 2026, compared: how each handles dbt models, query-time governance, AI grounded in the model, and embedded.
The best dashboard software in 2026 — Tableau, Power BI, Looker, Sigma, Metabase, Omni, Hex, and Cube — judged on what your dashboards run on.
A 2026 guide to embedded analytics platforms for SaaS — Cube, Sigma, Looker, ThoughtSpot, Sisense, GoodData, Metabase — matrix and how to choose.
Best Looker alternatives for AI analytics in 2026: Cube, Omni, Sigma, Metabase, Power BI, Tableau, ThoughtSpot — capability matrix and how to choose.
A 2026 guide to modern, warehouse-native BI tools — Cube, Omni, Sigma, Hex, Metabase, Lightdash, Looker — with a capability matrix and how to choose.
Best Power BI alternatives for modern BI teams in 2026: Cube, Omni, Sigma, Looker, Metabase, ThoughtSpot, Tableau — capability matrix and how to choose.
A 2026 comparison of semantic layers for AI and BI — Cube, dbt Semantic Layer, AtScale, Looker, Power BI, Databricks, Snowflake, and GoodData — with a capability matrix and how to choose.
A 2026 framework for deciding whether to build embedded analytics in-house or buy a platform: real costs, a decision table, and when each path wins.
Alternatives to the dbt Semantic Layer in 2026 — Cube Core, AtScale, LookML, and warehouse-native layers — with a capability matrix and how to choose.
A 2026 guide to adding AI-powered analytics inside your product: model metrics in a semantic layer, enforce multi-tenant security, embed, and ground the AI.
A 2026 step-by-step guide to building embedded analytics in a SaaS app: semantic layer, multi-tenant security, embed surfaces, caching, theming, and AI.
Why AI agents need a semantic layer, not raw text-to-SQL — governed metrics, compile-time governance, and MCP — and how to give an agent one in 2026.
Agentic analytics is AI-native BI where AI agents do the analytical work over a governed semantic layer, not raw tables. What it is and why it needs one.