Skip to content
Data warehouse ⇄ CRM

BigQuery to Kommo integration — real-time, two-way sync

Keep BigQuery and Kommo in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.

  • SOC 2 and 6 other compliance frameworks
  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect BigQuery and Kommo

Sync Kommo into BigQuery continuously and push warehouse results back onto CRM records, one two-way connection instead of two pipelines.

The CRM feeds the warehouse and the warehouse should feed the CRM: relationship data flows one way, and computed scores, segments, and customer context flow back. Most teams build the first half as a batch pipeline and never quite get to the second.

Stacksync does both with one connection. Companies, Pipelines & Statuses, Tasks, Notes from Kommo land in BigQuery as live tables, updated within seconds, and columns computed in BigQuery write back to fields in Kommo. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Replicate Kommo leads and contacts into a database or warehouse for reporting beyond built-in dashboards
  • 02 Push web form and product signups into Kommo pipelines as they are submitted so messenger-based follow-up starts quickly
  • 03 Feed ML feature tables in BigQuery from operational systems on a continuous schedule
  • 04 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs

Common sync patterns

Scores and segments back on the record

Lead scores, churn risk, or usage segments computed in BigQuery appear as fields in Kommo, where the people working accounts actually see them.

A single customer view

Join Kommo's relationship data with billing, product, and support data in BigQuery to build the customer picture the CRM alone cannot hold.

Cleanup that sticks

Deduplication and normalization done in BigQuery can be written back, so warehouse-side cleanup actually fixes the CRM.

What you can sync between BigQuery and Kommo

Representative objects on each side — any object or custom field can map to any target. Schemas are auto-detected; types are converted between the two systems.

BigQuery objects Kommo objects How this pairing syncs
Partitioned tables Synced like regular tables; partition columns map to target fields. Leads The central pipeline record; external signups and form fills sync in as leads. Partitioned tables is specific to BigQuery and Leads to Kommo — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Contacts Person records sync with other CRMs and databases for a shared contact file. Clustered tables is specific to BigQuery and Contacts to Kommo — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. Companies Organization records map to accounts in ERPs and invoicing tools. Datasets is specific to BigQuery and Companies to Kommo — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Pipelines & Statuses Stage definitions structure lead progress and drive stage-change syncs to reporting tools. Projects is specific to BigQuery and Pipelines & Statuses to Kommo — each maps to any object or custom field on the other side.
Tables The syncable unit: only tables can be synced per the Stacksync docs. Tasks Follow-up records keep rep activity consistent across systems. Tables is specific to BigQuery and Tasks to Kommo — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Kommo

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

BigQuery Kommo Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

DeliveryEach detected change is written to Kommo through its API, with automatic retries and rate-limit backoff.

Kommo BigQuery Sub-second propagation

DetectionKommo notifies Stacksync of record changes through webhook events. Webhooks on record add and update events, plus polling for backfill.

DeliveryEach detected change is applied to BigQuery as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
  • Kommo: Subject to per-account request rate limits.
What ships with BigQuery ⇄ Kommo

Connect BigQuery and Kommo for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Kommo connection.

Real-time

Two-way sync

Changes in BigQuery or Kommo instantly reflect in both systems. No stale data, no manual imports.

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Kommo data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.

At scale

Event queues

Handle millions of events per minute without losing a single BigQuery or Kommo record.

Observability

Monitoring

Track your BigQuery ⇄ Kommo sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between BigQuery and Kommo.

How the BigQuery and Kommo connectors work

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide

Kommo

Integration surface
REST API
Authentication
OAuth 2.0 with refresh tokens
Change detection
Webhooks on record add and update events, plus polling for backfill
Capabilities
read · write · webhooks
Rate limits
Subject to per-account request rate limits
How it works

How to connect BigQuery to Kommo — three steps, no code

Configure and sync within minutes, no code. Whether you sync 50k or 100M+ records, Stacksync handles the queues, infra, and plumbing. Integrations are non-invasive and need zero setup on your systems.

  1. 01

    Connect your apps

    Authenticate BigQuery and Kommo with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    BigQuery connected
    Kommo connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the BigQuery and Kommo objects to sync — Stacksync auto-detects both schemas, including custom fields where the platform exposes them. Sync to existing tables, or let Stacksync create new ones with ideal data types.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · BigQuery ⇄ Kommo
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    BigQuery Kommo
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
Try interactive demo
FAQ

BigQuery and Kommo integration FAQ

SECURITY

Security teams trust Stacksync

As a data company, we understand the importance of keeping your data secure. Stacksync is built with security best practices to keep your data safe at every layer, and is DPF-certified for US, EU, UK and CH data transfers.

Learn more about security
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH
→ SECURITY WITH BENEFITS

SSO & SCIM

Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.

Alerts

Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.

Secure connection options

Securely connects to your systems with:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 554 integrations available for BigQuery and Kommo.

Popular · 8 of 554
All BigQuery integrations All Kommo integrations Browse all integrations
Coworkers laughing in front of a laptop in a casual office setting

Your last integration took months.
Your next one takes a prompt.

Book a demo Tour the platform on your own