Real-time
Two-way sync
Changes in Gatekeeper or PostgreSQL instantly reflect in both systems. No stale data, no manual imports.
Business productivity ⇄ Database
Gatekeeper to PostgreSQL integration — real-time, two-way sync
Keep Gatekeeper and PostgreSQL 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 Gatekeeper and PostgreSQL
Mirror Gatekeeper's data into PostgreSQL so your own code can read and write it like any other table, with changes flowing both ways in seconds.
Engineers integrate with tools like Gatekeeper through APIs, which means auth, pagination, rate limits, webhooks, and retry logic, all maintained forever and all different for every tool. Meanwhile the data would be trivial to use if it simply lived in PostgreSQL.
Stacksync mirrors Users, Categories, Contracts, Vendors (Suppliers) from Gatekeeper into Materialized Views, Schemas, Columns, Primary and Unique Keys in PostgreSQL and keeps both sides in sync in real time. Your services query the database directly, and inserts or updates your code makes flow back into Gatekeeper, so the tool and the database never disagree.
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 Expose SaaS objects (CRM contacts, ERP invoices, support tickets) as Postgres tables that internal tools can query and join
- 04 Let an application write to its own database and have those rows appear as records in business systems in near real time
Common sync patterns
React to changes as they happen
Updates in Gatekeeper arrive as row changes in PostgreSQL, so triggers, jobs, and services can respond in near real time.
One integration pattern for the whole stack
Every synced tool looks the same from the database, so each new integration is configuration, not a new codebase.
Read Gatekeeper with a query
Records from Gatekeeper are ordinary rows in PostgreSQL; join them, index them, and use them in application logic without touching the vendor API.
What you can sync between Gatekeeper and PostgreSQL
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.
| Gatekeeper objects | PostgreSQL objects | How this pairing syncs | |
|---|---|---|---|
| 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. | Materialized Views Precomputed result sets synced outward on a refresh schedule. | Custom data groups is specific to Gatekeeper and Materialized Views to PostgreSQL — each maps to any object or custom field on the other side. | |
| Users Gatekeeper user and team records governed by role-based access; read to map contract and vendor owners, approvers, and internal contacts to CRM or HR records. | Schemas Namespaces that scope which tables a sync reads and writes. | Users is specific to Gatekeeper and Schemas to PostgreSQL — each maps to any object or custom field on the other side. | |
| Categories The classification taxonomy applied to contracts and vendors (type, department, business unit); synced so categorization stays consistent between Gatekeeper and downstream reporting or ERP dimensions. | Columns Field-level mapping targets; types are mapped to the connected system's field types. | Categories is specific to Gatekeeper and Columns to PostgreSQL — each maps to any object or custom field on the other side. | |
| 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. | Primary and Unique Keys Used as match keys for idempotent upserts and conflict resolution. | Contracts is specific to Gatekeeper and Primary and Unique Keys to PostgreSQL — each maps to any object or custom field on the other side. | |
| 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. | JSONB Columns Hold semi-structured payloads such as nested SaaS objects or metadata. | Vendors (Suppliers) is specific to Gatekeeper and JSONB Columns to PostgreSQL — each maps to any object or custom field on the other side. | |
| 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. | Sequences Generate surrogate keys for rows created by inbound syncs. | Files is specific to Gatekeeper and Sequences to PostgreSQL — each maps to any object or custom field on the other side. |
How changes propagate between Gatekeeper and PostgreSQL
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.
Gatekeeper PostgreSQL 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 PostgreSQL as a row-level write, with types converted between the two schemas.
PostgreSQL Gatekeeper Sub-second propagation
DetectionChanges in PostgreSQL are captured at the source via change data capture — no polling loop against its API. Logical replication (wal_level = logical) for change data capture via the "Postgres" connector.
DeliveryEach detected change is written to Gatekeeper through its API, with automatic retries and rate-limit backoff.
Rate-limit considerations
- 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.
- PostgreSQL: No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput.
What ships with Gatekeeper ⇄ PostgreSQL
Connect Gatekeeper and PostgreSQL for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Gatekeeper–PostgreSQL connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Gatekeeper or PostgreSQL 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 Gatekeeper or PostgreSQL record.
Observability
Monitoring
Track your Gatekeeper ⇄ PostgreSQL sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Gatekeeper and PostgreSQL.
How the Gatekeeper and PostgreSQL connectors work
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.
PostgreSQL
- Integration surface
- SQL wire protocol (PostgreSQL frontend/backend protocol)
- Authentication
- Database credentials (connection string or parameters), with optional SSL root certificate upload and optional SSH tunnel (SSH user + host); a least-privilege DB user
- Change detection
- Logical replication (wal_level = logical) for change data capture via the "Postgres" connector; database triggers (TRIGGER grant + stacksync_logging schema) via the trigger-based "Postgres Heroku" connector where
- Capabilities
- read · write · CDC
- Rate limits
- No API rate limits; throughput is bounded by connection limits, instance resources, and replication slot throughput
How it works
How to connect Gatekeeper to PostgreSQL — 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.
- 01
Connect your apps
Authenticate Gatekeeper and PostgreSQL with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Gatekeeper
connected
PostgreSQL
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Gatekeeper and PostgreSQL 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.
objects · Gatekeeper ⇄ PostgreSQLCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Gatekeeper PostgreSQLCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Gatekeeper and PostgreSQL integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Gatekeeper and PostgreSQL: authenticate both systems, choose the objects to sync (such as Gatekeeper's Custom data groups and Users), map fields visually, and changes propagate both ways in milliseconds — no code required.
-
Yes — Stacksync ships production-grade connectors for both Gatekeeper and PostgreSQL. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
-
Change detection on Gatekeeper: 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. On PostgreSQL: Logical replication (wal_level = logical) for change data capture via the "Postgres" connector; database triggers (TRIGGER grant + stacksync_logging schema) via the trigger-based "Postgres Heroku" connector where. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
-
On the Gatekeeper side: Users, Categories, Contracts, Vendors (Suppliers), plus custom fields where Gatekeeper exposes them. On the PostgreSQL side: Materialized Views, Schemas, Columns, Primary and Unique Keys. Stacksync auto-detects both schemas and converts types between the two systems.
-
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 Gatekeeper and PostgreSQL: React to changes as they happen; One integration pattern for the whole stack; Read Gatekeeper with a query. Updates in Gatekeeper arrive as row changes in PostgreSQL, so triggers, jobs, and services can respond in near real time.
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.
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 446 integrations available for Gatekeeper and PostgreSQL.
Popular · 4 of 446
No integrations match .
