
How to migrate from Looker to a modern BI tool: a practical playbook
A step-by-step Looker migration playbook: audit LookML, choose a replacement, rebuild dashboards, run cutover, and decommission. Includes a checklist.

A step-by-step Looker migration playbook: audit LookML, choose a replacement, rebuild dashboards, run cutover, and decommission. Includes a checklist.

Most SaaS teams track KPIs in a flat list. A metric tree connects a north-star metric to the drivers and inputs people can actually move. Here's how to build one.

Embed Basedash inside your own product so your customers get dashboards and the Basedash AI agent on their own data — without ever leaving you. Row-level security keeps every customer to their own data. Analytics, inside your product.

A practical guide to building a funnel analysis dashboard from event data. Covers SQL patterns, time windows, segmentation, layout, and the mistakes that quietly break funnel charts.
“Evaluamos Omni y otras herramientas de BI, pero la velocidad para llegar al insight con Basedash no tiene comparación.”
Greg Demoge
Cofundador y CPO · FullEnrich
Leer el caso de éxito →
“Para una empresa tan consciente de la seguridad como la nuestra, Basedash conectó al instante. Reportes que tomaban semanas están listos en horas.”
Claudio Godoy
AI Agents Lead · Taxfyle
Leer el caso de éxito →

How we replaced a one-size-fits-all subquery wrapper with an AST-based query rewriter, and finally got SQL Server, Spanner, and Athena pagination to actually work.

AI data analyst tools look similar in demos but behave very differently in production. Use this 5-dimension framework to evaluate 8 leading platforms in 2026.

A practical guide to building a Stripe revenue dashboard. Covers the data model, MRR and churn calculations, common gotchas, and tools for any team size.

Our pods were crash-looping and the logs said nothing. We chased an OOM, found something else entirely, and learned a Kubernetes default that should not be a default.

A practical playbook for cutting Snowflake, BigQuery, Redshift, and Databricks bills driven by BI dashboards. Diagnosis, SQL fixes, caching, Basedash Warehouse, and governance.

An intake and triage framework for data requests: a request taxonomy, a priority and effort matrix, an SLA model, and rules for what not to build.

A step-by-step guide to building a customer success dashboard for SaaS teams. Covers metrics, data sources, layout patterns, common mistakes, and rollout.

How modern BI teams version control dashboards, metrics, and semantic layers with Git. Compares Looker, dbt, Power BI projects, Cube, Hex, and AI-native tools, plus a maturity model.

A practical review workflow for AI-generated SQL: a ten-point checklist, the failure modes that show up most often, and a rubric for when to trust the query.

Skills are reusable bundles of instructions that every Basedash AI surface — chat, charts, dashboards, automations, insights, and tasks — can read on demand. Teach Basedash once, and it learns everywhere.

A practical playbook for diagnosing and fixing slow BI dashboards: SQL, warehouse tuning, caching, dashboard design, and tool-specific tips.

A practical framework for managing BI dashboard sprawl: a four-tier trust model, a 60-minute audit, retirement rules, and an ownership model that lasts.