Real-time
Two-way sync
Changes in Databricks or Snapfulfil instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ ERP
Databricks to Snapfulfil integration — real-time, two-way sync
Keep Databricks and Snapfulfil 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 Snapfulfil
Put Snapfulfil's records in Databricks as live tables for reporting and analysis, without extract jobs, and write results back where Snapfulfil can use them.
ERP data is some of the most asked-for data in the warehouse and some of the hardest to get: the record types are many, the APIs are strict, and extract jobs are brittle. Whether Snapfulfil carries financials, operations, workforce data, or all three, the analysis belongs in Databricks next to everything else the company measures.
Stacksync syncs Purchase orders / ASNs, Items (SKUs), Inventory balances, Shipments from Snapfulfil into tables in Databricks continuously, managing API limits and schema drift along the way. The connection is bi-directional, so values computed in Databricks can be written back to fields in Snapfulfil where that is useful.
Common use cases
- Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
- Keep inventory balances aligned between the WMS, the ERP, and selling channels to prevent oversells
- Sync the item master from the ERP so new SKUs are receivable in the warehouse immediately
Where Snapfulfil is the HR system of record: workforce analytics
Worker and organization data syncs into Databricks for headcount, cost, and planning analysis alongside other company data.
Where Snapfulfil runs operations: order and supply analysis
Operational records become queryable tables in Databricks, joinable with sales and finance data.
Group reporting across systems
Combine Snapfulfil's records with data synced from other systems in Databricks for consolidated views no single system can produce.
What you can sync between Databricks and Snapfulfil
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 | Snapfulfil objects | |
|---|---|---|
| Volumes Unity Catalog file storage used for staging bulk loads. | Purchase orders / ASNs Inbound expectations that drive receiving and putaway. | |
| SQL Warehouses The compute endpoint a sync connects to for query execution. | Items (SKUs) The item master, usually sourced from the ERP or storefront. | |
| Change Data Feed Row-level change records on Delta tables that drive incremental reads. | Inventory balances On-hand and allocated stock, read back to keep the ERP and selling channels accurate. | |
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Shipments Dispatch confirmations with carrier and tracking details returned to the order source. | |
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | Warehouse locations Bin and zone structure referenced by stock and movement records. | |
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Returns Inbound customer returns processed back into stock or quarantine. |
What ships with Databricks ⇄ Snapfulfil
Connect Databricks and Snapfulfil for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Snapfulfil connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Snapfulfil 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 Snapfulfil record.
Observability
Monitoring
Track your Databricks ⇄ Snapfulfil 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 Snapfulfil.
How the Databricks and Snapfulfil 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
Snapfulfil
- Integration surface
- REST-style web service API
- Authentication
- API credentials issued for the warehouse instance
- Change detection
- Polling, subject to the platform's API rate limits
- Capabilities
- read · write
- Rate limits
- Specific quotas are not publicly standardized; treat throughput as instance-dependent
How it works
How to connect Databricks to Snapfulfil — 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 Snapfulfil 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
Snapfulfil
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Snapfulfil 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 ⇄ SnapfulfilCustomers 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 SnapfulfilCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Snapfulfil integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Snapfulfil: authenticate both systems, choose the objects to sync (such as Databricks's Volumes and SQL Warehouses), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Databricks: Unity Catalog imposes a three-level namespace (catalog.schema.table) that governs access across workspaces. Snapfulfil: Its data model follows the warehouse order lifecycle (receive, putaway, pick, pack, ship), so integrations center on orders, stock, and shipments. Stacksync's field mapping accounts for these differences between Databricks and Snapfulfil without custom code.
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Stacksync is SOC 2 Type II and ISO 27001 certified with HIPAA BAA support. Data is encrypted in transit, and a zero-persistent-storage architecture means Databricks and Snapfulfil records are not retained after a sync operation.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Databricks and Snapfulfil connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Databricks–Snapfulfil integration in-house.
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Yes — Stacksync ships production-grade connectors for both Databricks and Snapfulfil. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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Change detection on Databricks: Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns. On Snapfulfil: Polling, subject to the platform's API rate limits. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
SECURITY
Security teams love 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 386 integrations available for Databricks and Snapfulfil.
Popular · 8 of 386
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Databricks ⇄ NetSuite -
Databricks ⇄ Dynamics 365 F&O - Databricks ⇄ SuiteAnalytics
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Databricks ⇄ Dynamics 365 Business Central -
Snapfulfil ⇄ BigQuery -
Snapfulfil ⇄ MotherDuck -
Snapfulfil ⇄ AWS S3 -
Snapfulfil ⇄ Actian Vector
No integrations match .
