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Data warehouse ⇄ Business productivity

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

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

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Why teams connect BigQuery and Gatekeeper

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

Whatever Gatekeeper 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.

Stacksync syncs Files, Workflow form data, Custom data groups, Users from Gatekeeper into tables in BigQuery continuously, handling schema, rate limits, and retries. Because the sync is bi-directional, results computed in BigQuery can also be written back into fields in Gatekeeper where the tool can use them.

Common use cases

  • 01 Sync Vendors (Suppliers) with a CRM or ERP so counterparty, compliance, contact, and spend records match across the VCLM and the systems of record.
  • 02 Push generated documents into Gatekeeper Files, or pull executed contract PDFs and compliance evidence out into a document store or archive.
  • 03 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
  • 04 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources

Common sync patterns

Cross-tool reporting

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

Where Gatekeeper accepts updates: operational write-back

Segments, scores, or reference values computed in BigQuery sync back onto records in Gatekeeper, putting analysis where the work happens.

History that outlives the tool

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

What you can sync between BigQuery and Gatekeeper

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 Gatekeeper objects How this pairing syncs
Tables The syncable unit: only tables can be synced per the Stacksync docs. Contracts The core contract records holding value, key dates, renewal terms, status, type, owner, and the linked vendor; created, read, updated, and deleted so contract data moves two-way between Gatekeeper and a database, ERP, or CRM. Tables is specific to BigQuery and Contracts to Gatekeeper — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Vendors (Suppliers) Company records for counterparties and suppliers with onboarding status, compliance, risk, contacts, and spend; read and written to keep vendor master data aligned with a CRM or ERP. Partitioned tables is specific to BigQuery and Vendors (Suppliers) to Gatekeeper — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Files Document files attached to contracts and vendors - executed PDFs, certificates, and compliance evidence; read to pull signed files and evidence out, or written to push generated documents in. Clustered tables is specific to BigQuery and Files to Gatekeeper — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. Workflow form data The structured data captured on Gatekeeper workflow forms (intake requests, vendor onboarding, risk assessments); exposed by the API since 2025 so form results sync into an operational database, not only contract and vendor records. Datasets is specific to BigQuery and Workflow form data to Gatekeeper — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Custom data groups Customer-configured custom fields and data groups; because the JSON:API and its docs are dynamic, any custom data added in Configuration exposes the same read/write endpoints as the standard objects and syncs the same way. Projects is specific to BigQuery and Custom data groups to Gatekeeper — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Gatekeeper

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

Gatekeeper BigQuery Interval-based propagation

DetectionStacksync polls Gatekeeper for changes on an incremental schedule, reading only records changed since the previous pass. No native developer webhook subscription API and no database change-data-capture log.

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.
  • Gatekeeper: Gatekeeper publishes no fixed public per-minute request quota; throughput is governed per key by its endpoint permissions, and every call is recorded (parameters, payload, response) under API Logs for monitoring. Pace bulk writes and use JSON:API pagination on list endpoints.
What ships with BigQuery ⇄ Gatekeeper

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Gatekeeper 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 Gatekeeper record.

Observability

Monitoring

Track your BigQuery ⇄ Gatekeeper 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 Gatekeeper.

How the BigQuery and Gatekeeper 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

Gatekeeper

Integration surface
RESTful API following the JSON:API specification, tenant-scoped with interactive docs at {tenant}.gatekeeperhq.com/api_docs and a published Postman collection. The API is dynamic: it exposes the standard Contract and Vendor objects plus any custom data groups and workflow-form data configured in the tenant.
Authentication
API keys created and managed under Configuration > API Keys and passed as a token; each key carries granular per-endpoint permissions set to read-only or write, so access is scoped per object. Multiple keys can be issued and revoked independently.
Change detection
No native developer webhook subscription API and no database change-data-capture log; detect changes by polling the JSON:API list endpoints filtered and sorted on updated-at timestamps. Gatekeeper's own event automation - Workflow Engine phase transitions and Interconnect process orchestration - runs inside the platform rather than as a subscribable webhook stream.
Capabilities
read · write
Rate limits
Gatekeeper publishes no fixed public per-minute request quota; throughput is governed per key by its endpoint permissions, and every call is recorded (parameters, payload, response) under API Logs for monitoring. Pace bulk writes and use JSON:API pagination on list endpoints.
How it works

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

    Choose tables

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

BigQuery and Gatekeeper 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
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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 443 integrations available for BigQuery and Gatekeeper.

Popular · 5 of 443
All BigQuery integrations All Gatekeeper integrations Browse all integrations
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