BI tools and a single set of definitions
🎯You'll be able to
Compare BI tools such as Power BI, Tableau, Looker Studio, Metabase and Superset, and keep metric definitions in one place so every report agrees.
🏠The everyday picture
A city's clocks should all show the same time. If each shop sets its own clock, meetings go wrong, however nice each clock looks.
📘What it really is
- BI tools connect to the warehouse and let people build and share dashboards:
- Power BI (Microsoft): strong with Excel and Microsoft 365; paid licences for sharing; check current terms.
- Tableau (Salesforce): flexible visual analysis; paid licences.
- Looker Studio (Google): free to use, easy with Google data; Looker is a separate paid product with a modelling layer.
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Metabase and Apache Superset: free, open source to run yourself; paid cloud versions exist.
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Choose by: who will edit and who will only view, licences per person, the warehouse you use, sign-in and access control, row-level security, and who will look after it.
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One set of definitions: if every report writes its own SQL for "net revenue", they drift apart. Put definitions in the warehouse marts (dbt) or a semantic layer (dbt's semantic layer, LookML, Power BI datasets, Cube), and build every report from them.
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Certified data: mark the official datasets and dashboards, so readers know which numbers are trusted.
- Static pages like the kit's dashboard are a fine start for small teams: no licences, easy to test, easy to keep in version control. The kit's tests check its page because a BI tool's dashboards can't be tested the same way here (BI tools weren't run: not verified).
📝A worked example
Input: SabziSathi's needs: 3 store managers who only view, 2 analysts who edit, a DuckDB warehouse today and maybe BigQuery later, and no budget yet.
Expected output: start with the kit's static dashboard, or Metabase run by the team (free, open source), reading the marts. Move to Looker
Studio or Power BI when the warehouse moves to the cloud, if the team's sign-in and licence costs fit. In every case, net revenue comes from
mart_daily_sales, not from SQL written in the tool. (A recommendation, not a measurement.)
🌍Where it's used
- Large companies: Power BI or Tableau with certified datasets.
- Startups: Metabase, Superset or Looker Studio.
- Data teams: dbt marts as the single source for every tool.
📝Case scenario
A retail chain had three BI tools and four definitions of "same-store sales". The CEO got different growth figures from finance and marketing in the same week. Defining it once in the warehouse and certifying one dataset made every tool show the same number.
🖱️Try it
Write a half-page recommendation of a BI tool for SabziSathi in a year's time, with 20 viewers and 4 editors on BigQuery. Mark prices as illustrative with the date and source. Success: your choice names who edits, who views, the cost basis and where definitions live.
⚠️Common mistakes
- Definitions written in each report. Sign: numbers drift between dashboards. Fix: marts or a semantic layer, used by every report.
- Choosing a tool by its demo. Sign: a beautiful tool nobody can afford for all viewers. Fix: compare licences for your real users.
- Everything editable by everyone. Sign: broken shared dashboards. Fix: editors and viewers, with certified versions.
🛠️For your project
The capstone's design notes ask for a BI tool choice and where SabziSathi's definitions will live.
✅Quick check
What's a semantic layer?
Show the answer
A shared place where metrics are defined once, used by every report and tool.
What should drive a BI tool choice?
Show the answer
Who edits and views, licences, the warehouse, access control and who looks after it.