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Analysis & BI guides
How to build a dashboard people use Design a dashboard around its audience and the decision it supports, keep it maintained, and retire it when nobody opens it anymore. How to build a metrics layer Define each metric once, in code, so every tool that queries revenue or churn gets the same number — and govern changes to it. How to build a modern data stack Ingestion, warehouse, transformation, BI and activation — what a data stack needs at each company stage, and what to defer. How to choose a notebook or data app tool Notebooks and data-app frameworks split by execution model, hosting, and audience — pick by who reads the output, not by chart style. How to choose a reverse ETL tool Pick a reverse ETL tool by where it runs relative to your warehouse, how syncs get built, and whether one-way sync is even enough. How to choose a spreadsheet analytics tool Spreadsheets remain the default analytics tool — choose by where the data comes from, who edits it, and how far you need to push beyond formulas. How to choose a visualization library Pick a visualization library by language, how much custom control you need, and whether commercial licensing is worth it — not by demo gallery polish. How to choose an embedded analytics platform Embedded analytics is bought by product teams, not analysts — choose by how it embeds, where tenant data lives, and how pricing scales per customer. How to choose an enterprise BI platform Governed enterprise BI is a different purchase than self-service BI — buy for semantic-layer governance, ecosystem fit, and AI maturity, not chart style. How to choose an internal tools platform Internal tool builders aren't analytics products — they're a UI on your warehouse. Choose by license, hosting, and who does the building. How to choose a BI tool Pick a BI tool by who builds, who reads, where metrics are defined and what viewers cost, not by the chart gallery in the demo.