LookML audit
Five days to get back a Looker people believe.
Full LookML audit in 5 business days: slow explores, fan-out joins, duplicated measures, runaway PDTs, abandoned dashboards. Prioritised report and fixes delivered on a separate branch, ready for review. Fixed scope, quote within 24 hours.
The problem
A two- or three-year-old Looker project accumulates the same debts: explores that take thirty seconds to answer, many-to-many joins without symmetric aggregates that inflate totals, the same measure defined three times with three filters, PDTs rebuilt every hour for nothing, dashboards nobody opens any more. The audit runs the project through a 25-point checklist, measures what actually costs you (response time, BigQuery slots, user trust) and ships the highest-return fixes directly in LookML, on a branch you review and merge.
Who it is for
- Data teams at scale-ups and mid-size companies running Looker for over a year
- A new data lead inheriting an undocumented LookML project
- Teams preparing Conversational Analytics or Gemini in Looker who want a sound base
- Subsidiaries that must account for the BigQuery cost of Looker
What you receive
- Full review of models, views, explores and dashboards (25-point checklist)
- Written report: findings ranked by impact, cause, recommended fix, effort
- Priority fixes applied in LookML on a separate branch, ready to review and merge
- Before / after measurement of the slowest explores
- 30-day remediation plan for the rest
- 20-minute video walking your team through the report
Out of scope
- Full remodelling (that is the Semantic layer offer)
- Building new dashboards
- Looker administration (licences, SSO, instance)
Prerequisites
- Developer access to the Looker instance and the LookML repository
- Read access to the BigQuery project (or the warehouse in use)
- One contact reachable in writing during the 5 days
How it runs
01
Day 1: written scoping
Brief, access, list of critical dashboards, System Activity extract.
02
Days 2 and 3: review
Models, explores, PDTs, datagroups, access, measured performance.
03
Day 4: fixes
Highest-return fixes shipped on a branch, with tests.
04
Day 5: handover
Report, 30-day plan, explainer video, written Q&A session.
StackLooker · LookML · BigQuery · Git · Looker API · System Activity
Frequently asked questions
Why five days, not more?
Because the scope is stable from one project to the next: one instance, one LookML repository. If your project exceeds 40 explores or 300 views, the quote says so and we split it.
Do we need a call?
No. The brief is written, the handover is written and on video. A 30-minute call is available if you want it.
What happens after the audit?
You merge the fixes and run the 30-day plan yourself, or you take the Looker retainer and Neuravoid does it. No obligation either way.
Do you work on warehouses other than BigQuery?
Yes, Snowflake and PostgreSQL behind Looker. The core of the audit is LookML, which does not depend on the warehouse.
Reference implementations
Code you can read before you write to us.
Three complete, public semantic layers built the way we build them for clients: dbt on BigQuery, LookML, dashboard, linter and continuous integration. One per sector.
E-commerce
Margin, average order value, repeat rate, one revenue number. Public thelook_ecommerce dataset, 13 metrics, 3 explores.
Healthcare
30-day readmissions, length of stay, chronic conditions, patient field protection. Synthetic Medicare data in OMOP format, 17 metrics.
Fintech
Payment volume, chargebacks, churn, loan book and delinquency. Generated synthetic data, customer history, 56 metrics, 134 tests.
Guides
9 min read · 5 September 2026
Looker vs Power BI: An Honest Comparison for a GCP Shop
Looker or Power BI when your data lives in BigQuery? Cost model, LookML versus DAX, governance, AI features in 2026, and the cases where Power BI wins.
Read the guide →10 min read · 5 September 2026
LookML Audit Checklist: the 25 Points to Check
The 25 checks of a LookML audit: Explores, joins and fan-out, symmetric aggregates, PDTs, datagroups, naming, duplicated measures, access grants, dashboards.
Read the guide →9 min read · 6 September 2026
Looker for e-commerce: margin, AOV, repeat, one revenue number
Ten e-commerce metrics defined once in dbt and exposed in Looker: net revenue, margin, AOV, repeat rate, returns, grain trap. With a public reference repo.
Read the guide →Describe your context
Three lines are enough: your stack, what is broken, the deadline. Written answer within one business day, call optional.
Request a quote