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Business productivity ⇄ Data warehouse

BetterContact to BigQuery integration — real-time data sync

Keep BetterContact and BigQuery 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
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Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect BetterContact and BigQuery

Get the data locked inside BetterContact into BigQuery as live tables, and send results back where BetterContact can use them, without writing a pipeline.

BetterContact is a read-only source: Stacksync reads its data in real time and delivers it into BigQuery, so BigQuery always reflects the current state of BetterContact — without exports, scripts, or schedulers.

Whatever BetterContact is used for, it accumulates data the rest of the company wants to analyze, and that data usually sits behind an API rather than in the warehouse. Building and babysitting an extraction pipeline is the tax most teams pay for it.

Common use cases

  • 01 Enrich new CRM leads with verified emails and mobile numbers as they are created, writing results back to the CRM record.
  • 02 Backfill a warehouse of contacts with waterfall enrichment across 20+ data providers.
  • 03 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
  • 04 Feed ML feature tables in BigQuery from operational systems on a continuous schedule

Common sync patterns

History that outlives the tool

A continuously synced copy in BigQuery preserves a queryable record even as data ages out of BetterContact or gets changed inside it.

Analytics on BetterContact's data

Records and events from BetterContact land in BigQuery as queryable tables, current within seconds and ready to join with the rest of the warehouse.

Cross-tool reporting

Combine BetterContact's data with data from every other synced system to answer questions no single tool can.

What you can sync between BetterContact and BigQuery

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.

BetterContact objects BigQuery objects How this pairing syncs
Lead Finder Search Prospect discovery by people and company filters, returning enriched lead profiles. Partitioned tables Synced like regular tables; partition columns map to target fields. Lead Finder Search is specific to BetterContact and Partitioned tables to BigQuery — each maps to any object or custom field on the other side.
Enrichment Request Batch or single submissions of up to 100 contacts per request for waterfall enrichment. Clustered tables Supported; clustering is transparent to the sync. Enrichment Request is specific to BetterContact and Clustered tables to BigQuery — each maps to any object or custom field on the other side.
Enriched Contact Verified work emails and mobile numbers with job title, LinkedIn profile, location, and skills. Datasets Organizational container — you pick which dataset’s tables to sync. Enriched Contact is specific to BetterContact and Datasets to BigQuery — each maps to any object or custom field on the other side.
Company Company data returned with each contact: name, domain, HQ location, industry, employee count. Projects Connection scope: the service account grants access per project. Company is specific to BetterContact and Projects to BigQuery — each maps to any object or custom field on the other side.

How changes propagate between BetterContact and BigQuery

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.

BetterContact BigQuery Sub-second propagation

DetectionBetterContact notifies Stacksync of record changes through webhook events. Job-based delivery — enrichment results arrive by webhook push or polling of the results endpoint.

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

BigQuery BetterContact 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").

DeliveryBetterContact does not accept inbound record writes, so this direction carries requests rather than records: BetterContact's output flows back as field updates on the originating BigQuery records.

Rate-limit considerations

  • BetterContact: Batches capped at 100 contacts per enrichment request.
  • 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.
What ships with BetterContact ⇄ BigQuery

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

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

Real-time

Real-time sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BetterContact or BigQuery 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 BetterContact or BigQuery record.

Observability

Monitoring

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

Trading partners

EDI

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

How the BetterContact and BigQuery connectors work

BetterContact

Integration surface
Asynchronous REST API: submit contacts, then receive results via webhook or fetch them from a results endpoint
Authentication
API key
Change detection
Job-based delivery — enrichment results arrive by webhook push or polling of the results endpoint; there is no persistent record store to watch
Capabilities
read · webhooks
Rate limits
Batches capped at 100 contacts per enrichment request.

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
How it works

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

    Choose tables

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

BetterContact and BigQuery 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 BetterContact and BigQuery.

Popular · 8 of 390
All BetterContact integrations All BigQuery integrations Browse all integrations
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