Two-way sync
Changes in BigQuery or Kommo instantly reflect in both systems. No stale data, no manual imports.
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.
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.
Lead scores, churn risk, or usage segments computed in BigQuery appear as fields in Kommo, where the people working accounts actually see them.
Join Kommo's relationship data with billing, product, and support data in BigQuery to build the customer picture the CRM alone cannot hold.
Deduplication and normalization done in BigQuery can be written back, so warehouse-side cleanup actually fixes the CRM.
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 | |
|---|---|---|
| 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. | |
| Clustered tables Supported; clustering is transparent to the sync. | Contacts Person records sync with other CRMs and databases for a shared contact file. | |
| Datasets Organizational container — you pick which dataset’s tables to sync. | Companies Organization records map to accounts in ERPs and invoicing tools. | |
| 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. | |
| Tables The syncable unit: only tables can be synced per the Stacksync docs. | Tasks Follow-up records keep rep activity consistent across systems. |
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every BigQuery–Kommo connection.
Changes in BigQuery or Kommo instantly reflect in both systems. No stale data, no manual imports.
Trigger automated workflows whenever BigQuery or Kommo data changes, update records, fire webhooks, or kick off sequences without brittle API scripts.
Handle millions of events per minute without losing a single BigQuery or Kommo record.
Track your BigQuery ⇄ Kommo sync health, view errors, and replay failed events in one click.
Transform legacy EDI complexity into simple database interactions between BigQuery and Kommo.
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.
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.
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.
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Yes. Stacksync provides a managed, real-time two-way integration between BigQuery and Kommo: authenticate both systems, choose the objects to sync (such as BigQuery's Partitioned tables and Clustered tables), map fields visually, and changes propagate both ways in milliseconds — no code required.
Yes. Each object mapping can be bidirectional or restricted to a single direction (both systems accept writes). Read-only mirrors, one-way pushes, and full two-way sync can be mixed in the same integration.
Common patterns for BigQuery and Kommo: Scores and segments back on the record; A single customer view; Cleanup that sticks. Lead scores, churn risk, or usage segments computed in BigQuery appear as fields in Kommo, where the people working accounts actually see them.
BigQuery: 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. Kommo: REST API. Authentication: OAuth 2.0 with refresh tokens. Stacksync manages authentication, retries, and rate limits on both sides.
Kommo: Kommo is the former amoCRM, and its data model centers on leads moving through configurable pipelines and statuses, with contacts and companies linked to each lead. BigQuery: Views and materialized views are not supported — only tables. Stacksync's field mapping accounts for these differences between BigQuery and Kommo without custom code.
Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means BigQuery and Kommo records are not retained after a sync operation.
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.
Let your users access Stacksync from your centralized user management systems. Works with Okta, Azure, Google SSO and more.
Immediately get alerted about record syncing issues over email, Slack, PagerDuty and WhatsApp. Resolve issues from a centralized dashboard with retry and revert options.
Securely connects to your systems with:
Every pair below is a real-time, two-way sync. Search all 386 integrations available for BigQuery and Kommo.