How do we pick between Looker, Metabase, and Superset?

Choose based on governance needs and existing skills. If you need strong modeling, governance, and a mature semantic layer, Looker can be worth the spend. Metabase is great for quick wins with permissions and ease of use. Superset + a headless semantic layer (Cube/dbt metrics) is flexible for engineering‑heavy teams. In all cases, enforce RLS in the warehouse and keep definitions out of the BI UI.

Do we need both dbt tests and Great Expectations?

For critical domains, yes. dbt tests are great for schema and column‑level assertions tied to transformations. Great Expectations (or Soda) complements with cross‑table business rules and profile‑based anomaly detection. Many teams start with dbt tests and add a suite where incidents have actually happened.

What SLOs should we start with?

Begin with freshness (e.g., max 15 minutes lag for fct_orders during business hours) and accuracy (e.g., reconciling totals within 0.5% to the ledger). Add completeness (expected rows vs. source) once you have contracts. Make them visible in the catalog and page the owning team on breach.

How do we keep analysts from creating shadow datasets?

Give them certified, well‑modeled explores and the ability to prototype in sandboxes with quotas. Enforce deprecation rules and cost visibility. Most shadow data is a symptom of slow pipelines or missing definitions—fix those first, then require all production dashboards to depend on certified models via dbt exposures.

Can we do this without a data catalog?

You can start, but you won’t scale trust. At minimum, stand up OpenMetadata or DataHub, ingest dbt docs and lineage, and attach owners/SLOs. It becomes the social contract that makes self‑service viable.

Data-engineering · Oct 23, 2025 · 10 minute read

Self‑Service Analytics Without the Data Hangover: How We Built a Trustworthy Visualization Platform That Scales

Your BI tool isn’t the bottleneck—your reliability is. Here’s the playbook we use to turn “spreadsheet theater” into a self‑service platform leaders can actually bet the quarter on.

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Self‑Service Analytics Without the Data Hangover: How We Built a Trustworthy Visualization Platform That Scales

Key takeaways

Implementation checklist