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. 😉
Business intelligence tools turn warehouse data into reports, dashboards, and answers. What the categories are, how they work, and how AI is changing them.
A semantic layer defines metrics, dimensions, joins, and access rules once, so BI, embedded apps, and AI agents all return the same governed numbers.
Data analytics turns raw data into decisions. The four types, the workflow, the tools, and how AI agents are changing analytical work over a semantic layer.
Data modeling defines your entities, attributes, relationships, metrics, and join paths so data is stored consistently and queried the same way everywhere.
Data visualization turns data into charts, dashboards, and maps. A guide to chart types, design principles, tools, and the governed data behind every visual.
Embedded analytics puts dashboards and AI-driven insights inside your own product, governed per customer by row-level security and a shared semantic layer.
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.