Real-time
Two-way sync
Changes in Databricks or Gatekeeper instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Business productivity
Databricks to Gatekeeper integration — real-time, two-way sync
Keep Databricks 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.
- 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 Databricks and Gatekeeper
Get the data locked inside Gatekeeper into Databricks 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 Users, Categories, Contracts, Vendors (Suppliers) from Gatekeeper into tables in Databricks continuously, handling schema, rate limits, and retries. Because the sync is bi-directional, results computed in Databricks 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 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- 04 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
Common sync patterns
Analytics on Gatekeeper's data
Records and events from Gatekeeper land in Databricks as queryable tables, current within seconds and ready to join with the rest of the warehouse.
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 Databricks sync back onto records in Gatekeeper, putting analysis where the work happens.
What you can sync between Databricks 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.
| Databricks objects | Gatekeeper objects | How this pairing syncs | |
|---|---|---|---|
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | 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. | Catalogs is specific to Databricks and Users to Gatekeeper — each maps to any object or custom field on the other side. | |
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | 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. | Schemas is specific to Databricks and Categories to Gatekeeper — each maps to any object or custom field on the other side. | |
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | 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. | Delta Tables is specific to Databricks and Contracts to Gatekeeper — each maps to any object or custom field on the other side. | |
| Views Curated read-only projections used as sync sources for downstream tools. | 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. | Views is specific to Databricks and Vendors (Suppliers) to Gatekeeper — each maps to any object or custom field on the other side. | |
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | 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. | Materialized Views is specific to Databricks and Files to Gatekeeper — each maps to any object or custom field on the other side. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | 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. | Volumes is specific to Databricks and Workflow form data to Gatekeeper — each maps to any object or custom field on the other side. |
How changes propagate between Databricks 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.
Databricks Gatekeeper Sub-second propagation
DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.
DeliveryEach detected change is written to Gatekeeper through its API, with automatic retries and rate-limit backoff.
Gatekeeper Databricks 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 Databricks as a row-level write, with types converted between the two schemas.
Rate-limit considerations
- Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
- 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 Databricks ⇄ Gatekeeper
Connect Databricks and Gatekeeper for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Gatekeeper connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks 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 Databricks or Gatekeeper record.
Observability
Monitoring
Track your Databricks ⇄ Gatekeeper sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Databricks and Gatekeeper.
How the Databricks and Gatekeeper connectors work
Databricks
- Integration surface
- SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
- Authentication
- Personal access tokens or OAuth machine-to-machine credentials for service principals
- Change detection
- Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
- Capabilities
- read · write · CDC
- Rate limits
- Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits
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 Databricks 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.
- 01
Connect your apps
Authenticate Databricks 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.
Databricks
connected
Gatekeeper
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks 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.
objects · Databricks ⇄ GatekeeperCustomers 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.
Databricks GatekeeperCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Gatekeeper integration FAQ
-
Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Gatekeeper: authenticate both systems, choose the objects to sync (such as Databricks's Catalogs and Schemas), map fields visually, and changes propagate both ways in milliseconds — no code required.
-
Change detection on Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. 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. 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 Databricks side: Catalogs, Schemas, Delta Tables, Views. 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 Databricks and Gatekeeper: Analytics on Gatekeeper's data; Cross-tool reporting; Where Gatekeeper accepts updates: operational write-back. Records and events from Gatekeeper land in Databricks as queryable tables, current within seconds and ready to join with the rest of the warehouse.
-
Databricks: SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution. Authentication: Personal access tokens or OAuth machine-to-machine credentials for service principals. Gatekeeper: 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. Stacksync manages authentication, retries, and rate limits on both sides.
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 451 integrations available for Databricks and Gatekeeper.
Popular · 4 of 451
No integrations match .
