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Data warehouse ⇄ CRM

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

Keep BigQuery and Vitally 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

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Why teams connect BigQuery and Vitally

Sync Vitally 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. Custom Trait, Account, User, Organization from Vitally land in BigQuery as live tables, updated within seconds, and columns computed in BigQuery write back to fields in Vitally. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Keep CS tasks and notes aligned between Vitally and ticketing or project tools.
  • 02 Sync account health scores and lifecycle stages from Vitally into a CRM so sales sees churn risk before renewals.
  • 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

Cleanup that sticks

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

CRM analytics on live data

Accounts, contacts, and activity from Vitally are queryable in BigQuery moments after they change, so dashboards stop lagging the reality they describe.

Scores and segments back on the record

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

What you can sync between BigQuery and Vitally

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 Vitally objects How this pairing syncs
Clustered tables Supported; clustering is transparent to the sync. Conversation Customer conversations logged in Vitally; activity objects include parent object details in the payload. Clustered tables is specific to BigQuery and Conversation to Vitally — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. NPS Response NPS survey responses for account-health reporting. Datasets is specific to BigQuery and NPS Response to Vitally — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Custom Trait Custom account and user traits for segmentation. Projects is specific to BigQuery and Custom Trait to Vitally — 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. Account Core customer account records with health scores and lifecycle traits; created, updated, retrieved, and listed via the REST API. Tables is specific to BigQuery and Account to Vitally — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. User End users tied to accounts, including activity and custom traits. Partitioned tables is specific to BigQuery and User to Vitally — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Vitally

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 Vitally 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 Vitally through its API, with automatic retries and rate-limit backoff.

Vitally BigQuery Sub-second propagation

DetectionVitally notifies Stacksync of record changes through webhook events. Incremental polling on updatedAt cursors.

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.
  • Vitally: Default rate limit of 1,000 requests/min (token bucket); write operations consume more budget, headers expose remaining quota.
What ships with BigQuery ⇄ Vitally

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Vitally 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 Vitally record.

Observability

Monitoring

Track your BigQuery ⇄ Vitally 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 Vitally.

How the BigQuery and Vitally 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

Vitally

Integration surface
REST API with cursor-based pagination (sortable by createdAt/updatedAt)
Authentication
API key via Basic Auth; keys created in Settings -> Integrations -> REST API and individually revocable
Change detection
Incremental polling on updatedAt cursors; playbook-triggered webhooks can push events for near real-time updates
Capabilities
read · write · webhooks
Rate limits
Default rate limit of 1,000 requests/min (token bucket); write operations consume more budget, headers expose remaining quota.
How it works

How to connect BigQuery to Vitally — 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 Vitally 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
    Vitally connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the BigQuery and Vitally 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 ⇄ Vitally
    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 Vitally
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

BigQuery and Vitally 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
CSA STAR
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 390 integrations available for BigQuery and Vitally.

Popular · 8 of 390
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