Analytics
Connect BigQuery to AI Inbox, agents and automation without rebuilding your current stack.
Category
Analytics
Auth type
service_account
Sync direction
outbound
Setup time
35 minutes
Health checks
Included
Objects
3 synced
Live health
Status is powered by separate read-only health checks and never treats missing data as success.
Uptime 30d
No data
Avg latency
No data
Active workspaces
Collecting
Gray bars mean days without data. History will fill automatically after the next health checks.
What it does
The scenarios, limits and implementation notes live in MDX so the page stays SEO-ready and easy to update.
Official BigQuery websiteBigQuery in DarhimLabs connects analytics with AI Inbox, agents and automation. The most common scenario is lead attribution, ROI reporting, campaign quality scoring, but the connector is not just a simple "send event". DarhimLabs maintains object mapping, last sync status, health check scopes and audit log of configuration changes in one place.
In practice, the team sees customer conversation, AI recommendations, integration status and sync result without switching between tools. If an AI agent qualifies a lead, escalates a case or updates data, BigQuery receives only the fields explicitly selected in workspace configuration.
Use BigQuery when your team already works in this tool and you want DarhimLabs to act as an AI layer over your current stack. The integration is particularly useful for flows: lead attribution, ROI reporting, campaign quality scoring.
The biggest return appears when customer conversations create real operational tasks: a lead needs to reach sales, a case needs support, a document needs the knowledge base, a payment needs verification or an appointment needs the calendar. DarhimLabs enforces the rule: first context and workflow approval, only then write to external system.
Synchronization covers objects: Campaigns, Attribution events, Conversion metrics. Each object has explicit sync direction, fields, trigger and implementation note in integration data table.
Typical flow looks like this: conversation arrives in AI Inbox, agent evaluates intent, DarhimLabs records summary and confidence score, and connector sends only approved fields to BigQuery. For outbound write, retry/backoff and delivery log are available, and for inbound read, source freshness is visible.
Before production launch, run token test, field mapping test and webhook event test. All errors go to audit log and health check section in integration dashboard.
Recommended rollout sequence:
Objects & field sync
| Object | Direction | Fields | Trigger | Notes |
|---|---|---|---|---|
| Campaigns | outbound | campaign_id, source, cost, lead_score | campaign synced | Field mapping is configurable per workspace. |
| Attribution events | inbound | event, timestamp, conversion_value | conversion tracked | Field mapping is configurable per workspace. |
| Conversion metrics | outbound | report_id, metric, period | report requested | Field mapping is configurable per workspace. |
Setup guide
Every step has a concrete goal, and the validation step does not create production data.
Go to Dashboard -> Integrations -> Bigquery.
Use OAuth, API key or webhook setup depending on the connector. DarhimLabs does not store user passwords.
Grant only the scopes required for the selected workflow. Each scope has a business explanation.
Choose how DarhimLabs fields map to Bigquery objects.
The health check verifies token, scopes, sample read and test event without creating production data.
Screenshots
Screenshoty pokazuja realny przeplyw DarhimLabs: overview, autoryzacje, mapping, health check i logike setupu.
Demo embed
Pokaz BigQuery + DarhimLabs w praktyce bez formularza i bez logowania. Embed otwiera pelne demo w nowej karcie, a iframe wysyla resize event do strony nadrzednej.
HTML iframe
<iframe
src="https://demo.darhimlabs.pl/embed/ecommerce"
width="100%"
height="600"
frameborder="0"
loading="lazy"
title="Demo DarhimLabs - ModaNova Store"
></iframe>React component
import { DarhimLabsDemoEmbed } from "@darhimlabs/demo-embed";
<DarhimLabsDemoEmbed story="ecommerce" autoresize />Permissions
Kazdy scope ma opis biznesowy. Write access jest pokazany jawnie.
| Scope | Access | Why needed | Sensitive |
|---|---|---|---|
| analytics.read | read | Read access is used to match records and prevent duplicates in Bigquery. | Nie |
| events.write | write | Write access is used only for approved workflow actions in Bigquery. | Tak |
| reports.read | read | Read access is used to match records and prevent duplicates in Bigquery. | Nie |
Security notes
The integration page helps IT teams assess risk before the trial, not only during procurement.
OAuth/API secrets are stored as references and can be revoked without deleting sync history.
The audit log records authorization, field mapping changes, retries and health check errors.
Health checks verify tokens, scopes and a test event without creating production data.
Workspace-level RLS limits access to connector configuration and sync logs.
BigQuery has an explicit Beta status, so we do not hide whether the connector is native, beta, manual or webhook-based.
Recommended stack
API & custom extension
Jezeli natywny connector nie pokrywa calego workflow, doloz REST API, OpenAPI spec albo webhooki v2 z HMAC, retry/backoff i sandbox test delivery.
Tworzenie i aktualizacja webhook endpointow, custom actions i integracji workspace.
Specyfikacja pod codegen, review security i automatyczne testy kontraktow.
Eventy v2 dla leadow, rozmow, handoffow, health checkow i sync statusu.
Kazdy delivery moze byc podpisany naglowkiem DL-Signature z timestampem.
Retry/backoff dla bledow 5xx i timeoutow: 1 min, 5 min, 15 min, 1h, 24h.
Sandbox payload, test delivery i replay bez dotykania produkcyjnych danych.
Event catalog
{
"id": "evt_01J...",
"type": "conversation.handoff_requested",
"created_at": "2026-05-07T12:00:00Z",
"data": {
"integration": "bigquery",
"workspace_id": "wrk_...",
"conversation_id": "conv_...",
"lead_score": 87
}
}Headers
Content-Type: application/jsonUser-Agent: DarhimLabs-Webhooks/2.0DL-Event: conversation.handoff_requestedDL-Signature: t=timestamp,v1=hmac_sha256Case study
The team had customer context split across tools and could not reliably audit AI-assisted decisions.
Operations team
DarhimLabs connected Bigquery, mapped the key objects and added health checks, webhooks and audit logging.
"The integration made AI operations measurable instead of another disconnected automation."
1 day
Setup time
42%
Manual routing reduced
100%
Audit coverage
FAQ
Not by default. DarhimLabs updates only fields selected in field mapping and keeps an audit log of every change.
Yes. You can limit synchronization by status, score, channel, tag, workspace or automation condition.
Yes. Permissions are described scope by scope, and the Bigquery integration can run read-only if the workflow does not need writes.
It verifies token status, required scopes, last synchronization and rate-limit errors without creating production data.
Yes. Key lifecycle events are available through DarhimLabs API and webhooks v2.
Most teams complete the first setup in minutes. Enterprise rollouts can include a shared implementation plan.
Yes. You can run test delivery, replay the last event and inspect HMAC logs without sending production data.
Yes. DarhimLabs shows the last health check, token status, missing scopes and last synchronization error.
Related integrations
CRM
Sync contacts, companies, deals and lifecycle stages between HubSpot and DarhimLabs.
View setup
CRM
Connect AI conversations with Salesforce leads, contacts, opportunities and routing.
View setup
CRM
Create leads, activities and deals in Pipedrive after conversation qualification.
View setup
CRM
Sync contacts and sales tasks in Zoho CRM.
View setup
Communication
Voice, SMS and WhatsApp transport for AI agents and reminders.
View setup
Communication
Conversations, template messages, opt-in and SLA in one inbox.
View setupStart a trial, book a demo, or build a custom extension through the API.