How do we keep metric definitions consistent across tools?

Adopt a semantic layer (`dbt metrics`, `LookML`, or `Cube`) and force certified dashboards to query it. Version metric definitions in Git, require PR reviews, and auto‑generate docs. Block direct joins to raw tables for certified content.

What’s the fastest way to add data quality without a platform rewrite?

Start by tagging your top 10 Gold tables and add `dbt` tests for not‑null, unique keys, and referential integrity. Wire failures to Slack/PagerDuty. Then add `Great Expectations`/`Soda` for distribution checks and `OpenLineage` for blast‑radius analysis.

Should we go streaming or stay batch for self‑service BI?

Use streaming where freshness SLO or product requirements demand it (fraud, ops). Most BI can run on micro‑batch (5–15 min) with incremental models and still meet SLOs at lower cost. Don’t over‑engineer Kafka because it’s trendy.

How do we prevent analysts from creating expensive, fragile dashboards?

Give them certified marts and semantic models with sample data by default, enforce RLS/CLS and budgets, and disable direct access to raw layers. Provide office hours and lint their queries via data review PRs.

Can we let LLMs query the warehouse safely?

Only through the semantic layer with strict scopes and guardrails (whitelisted metrics, row limits, read replicas). Log prompts/queries, add cost caps, and never let an LLM improvise joins on Bronze data.

Data-engineering · Oct 2, 2025 · 10 minute read

Self‑Service Analytics Without the Dumpster Fire: Building a Visualization Platform People Actually Trust

If your dashboards are fast but wrong, you don’t have self‑service—you have self‑sabotage. Here’s how to build a reliable, governed visualization stack that ships business value.

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Self‑Service Analytics Without the Dumpster Fire: Building a Visualization Platform People Actually Trust

Key takeaways

Implementation checklist