Migration to Looker
Change tools without copying the mess.
Migration to Looker from Looker Studio, Power BI or Tableau: report inventory by real usage, dbt modelling on BigQuery and LookML, reference dashboards rebuilt and reconciled, analyst training. 4 to 8 week engagement, quoted after the inventory.
The problem
A BI migration fails when the 200 existing reports are copied one by one. In most instances, 80% of dashboards have not been opened in three months: we do not migrate them. The engagement starts with the real inventory (who opens what, how often, for which decision), models the surviving metrics once, in dbt on BigQuery and then in LookML, rebuilds the reference dashboards and runs them alongside the old tool until the numbers match and the gaps are explained. dbt modelling is included whatever the source, Looker Studio, Power BI or Tableau: it is what stops the business logic hidden in old reports from being copied over. Analysts are trained on explores so ad-hoc requests stop landing on you. Looker Studio (renamed Data Studio in 2026) can stay for personal exploration: it connects to the same layer.
Who it is for
- Teams on Looker Studio hitting governance, diverging definitions or row-level access limits
- Companies on Tableau or Power BI consolidating on Google Cloud who want one semantic layer
- Scale-ups whose Looker licence is signed but whose LookML project has not started
- Data leaders who want fewer reports, not relocated ones
What you receive
- Usage inventory of existing reports (logs, written interviews) and the list of what does not migrate
- LookML model: views, explores per domain, single measures, drill-downs, role-based access
- dbt modelling on BigQuery of facts and dimensions, included whatever the source (Looker Studio, Power BI, Tableau)
- Five to ten reference dashboards rebuilt in Looker and reconciled number by number with the old tool
- Documented parallel run, gaps explained and signed off
- Asynchronous analyst training on explores (videos, exercises, review of their first explores)
- Decommissioning plan for the old tool
Out of scope
- Looker licences and negotiation with Google
- Like-for-like migration of unused reports
- Ingesting new sources into BigQuery (scoped separately)
Prerequisites
- Source data already in BigQuery, or an ingestion plan under way
- A provisioned (or in-progress) Looker instance and a Git repository
- One business owner per domain, reachable in writing
How it runs
01
Weeks 1 and 2: inventory
Real report usage, metrics to keep, sources, non-migration decisions approved in writing.
02
Weeks 3 to 5: modelling
dbt and LookML, first domain shipped by end of week 3, tested explores.
03
Weeks 5 to 7: reference dashboards
Rebuild, number-by-number reconciliation, parallel run, gaps explained.
04
Week 8: cut-over
Training, documentation, decommissioning plan, two weeks of written questions.
StackLooker · LookML · BigQuery · dbt · Looker Studio / Data Studio (source) · Tableau, Power BI (source)
Frequently asked questions
Why not migrate everything?
Because a report nobody opens costs maintenance and trust. The inventory is written and approved: what does not migrate is archived, not lost.
How long do both tools run in parallel?
Two to four weeks per domain, until the numbers match and the remaining gaps are explained and signed off by the business.
What happens to Looker Studio?
It can stay for personal exploration, connected to the semantic layer. What disappears is the source-of-truth role it should never have had.
How is the quote set?
On the number of domains and reference dashboards. A first quote goes out within 24 hours from your description; it is confirmed after the week 2 inventory. Beyond two domains we split into lots.
Reference implementations
Code you can read before you write to us.
Three complete, public stacks 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
8 min read · 5 September 2026
Looker vs Looker Studio: Which One You Need and When to Switch
Looker and Looker Studio solve different problems. Governance, semantic layer, row-level security, pricing, Looker Studio Pro, and when it is time to switch.
Read the guide →9 min read · 5 September 2026
Migrate from Looker Studio to Looker: When, How, How Much
When to leave Looker Studio for Looker, the migration steps, what to model first, the pitfalls, and realistic timeline and budget ranges for a remote delivery.
Read the guide →6 min read · 18 September 2026
Looker Consultant in 2026: Scope, Rates, Deliverables
What a Looker consultant costs in the US and UK in 2026, what a good one delivers, the red flags to avoid, and when a fixed-price audit beats an hourly rate.
Read the guide →Describe your context
Three lines are enough: your systems, what is broken, the deadline. Written answer within one business day.
Request a quote