01
Your marts, as-is
Kimo reads fct_ and dim_ tables directly and lets you certify measures on top.
dbt-friendly
WarehousesPopular
BigQuery + Kimo
Query BigQuery live. Kimo writes the SQL, pushes it down and caches results. No copies, no stale extracts.
- Auth
- Read-only credentials
- Sync
- Live query
- Setup
- ≈ 10 min
Live demo workspace with fictional data · no signup, no credentials needed
kimo / connectors / bigquerySyncing
Sync loglive query
- Succeeded:analytics.fct_revenue+2,180 rowsnow
- Succeeded:ga4_export.events_*+1,399 rows2m ago
- Succeeded:analytics.dim_accounts+618 rows4m ago
- Succeeded:analytics.fct_revenue+2,237 rows6m ago
Rows synced · 30 days
147.9M
Workspaces
44%
What you can do
What teams build with BigQuery
BigQuery on its own answers half the question. Joined with the rest of your stack in Kimo, it answers the other half.
02
Cost-aware live queries
Partition pruning, result caching and per-dashboard byte budgets keep the bill in check.
Bytes scanned ↓ 71%
03
GA4 export, modeled
Turn the raw GA4 export into sessions, funnels and attribution without writing UNNEST.
events_*
Objects & tables
Exactly what gets synced
Kimo maps BigQuery into clean, typed tables with primary keys and incremental cursors, so syncs stay fast and joins just work.
3 tables · 13 fields
Tables
analytics.fct_revenue
Live query≈ 497,000 rows| Field | Type | Notes |
|---|---|---|
| date | date | Incremental cursor |
| account_id | id | References account |
| mrr | decimal | Measure |
| plan | enum | Low-cardinality dimension |
| region | string |
Custom fields and extra objects are discovered automatically on each sync. Row counts are illustrative.
Sample model
From raw BigQuery tables to a certified metric
A starter model Kimo suggests the moment BigQuery is connected. Every join is editable.
BigQuery · Revenue by segment
Orders fact table joined with the customer dimension and CRM accounts.
- BigQueryanalytics.fct_revenue
- HubSpotcompanies
- Google Sheetstargets
Model
customer_id
Measuresrevenueorders
Revenue · last 30 days
$57.4K-4.1% wk/wk
Fictional data · hover the chart for daily values
Setup
Connect BigQuery in 10 min
No engineers, no pipelines to maintain. Kimo asks for the minimum access it needs and tells you exactly what it will read.
- 1
Create a dedicated role
Grant SELECT on the schemas Kimo should see, and a small warehouse for its queries.
- 2
Open network access
Allow Kimo’s static egress IPs, use an SSH tunnel, or run the on-prem agent inside your network.
- 3
Enter connection details
Credentials are encrypted at rest with a per-workspace key and tested before saving.
- 4
Pick schemas
Kimo reads the information schema and suggests models from your marts.
Grant the service account
gcloud projects add-iam-policy-binding northwind-analytics \
--member=serviceAccount:[email protected] \
--role=roles/bigquery.dataViewerRead-only, encrypted, revocable. Credentials are encrypted with a per-workspace key, never logged, and can be rotated without breaking your models.
Connect BigQuery
Step 2 of 3 · Kimo demo workspace
- Reaching host
- Authenticating
- Reading schema
Read-only access
Illustration only · placeholder values, never real secrets
Works great with
Pair BigQuery with the rest of your stack
Kimo suggests these joins automatically when both sources are connected.
FAQ
BigQuery questions, answered
BigQuery is queried live: Kimo compiles each chart to SQL and runs it on your warehouse, with a short result cache you control per dashboard.
BigQuery · Read-only credentials · Live query
See your BigQuery data in Kimo in 10 min.
Try it on the live demo workspace first, then connect your own account when you are ready.