kimo
Open demo
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
Request access

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%
Simulated demo data
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.

01

Your marts, as-is

Kimo reads fct_ and dim_ tables directly and lets you certify measures on top.

dbt-friendly
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

analytics.fct_revenue

Live query≈ 497,000 rows
FieldTypeNotes
datedateIncremental cursor
account_ididReferences account
mrrdecimalMeasure
planenumLow-cardinality dimension
regionstring
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.

Template
  • BigQuery
    analytics.fct_revenue
  • HubSpot
    companies
  • Google Sheets
    targets
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. 1

    Create a dedicated role

    Grant SELECT on the schemas Kimo should see, and a small warehouse for its queries.

  2. 2

    Open network access

    Allow Kimo’s static egress IPs, use an SSH tunnel, or run the on-prem agent inside your network.

  3. 3

    Enter connection details

    Credentials are encrypted at rest with a per-workspace key and tested before saving.

  4. 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:kimo-reader@northwind-analytics.iam.gserviceaccount.com \
  --role=roles/bigquery.dataViewer

Read-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
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.

Request access