Real-time
Two-way sync
Changes in Databricks or MongoDB instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Databricks to MongoDB integration — real-time, two-way sync
Keep Databricks and MongoDB 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
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Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Databricks and MongoDB
Connect MongoDB and Databricks with one live, two-way sync: operational rows flow into the warehouse, and computed results flow back where systems can read them fast.
Operational databases and analytical warehouses want the same data at different moments. Analysts want MongoDB's rows in Databricks, current and joinable, without a change-data-capture pipeline to maintain. Engineers want the outputs of warehouse work, such as aggregates, features, and segments, available in MongoDB where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in MongoDB sync into Databricks in real time, and result tables in Databricks sync back into MongoDB, with schema and type mapping between the two systems handled for you.
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 a MongoDB-backed product catalog aligned with an ERP's item master in both directions.
- Consolidate documents from multiple clusters or tenants into a single warehouse-facing store.
Serve warehouse results at database speed
Aggregates or model outputs computed in Databricks sync into MongoDB, where whatever reads from that database gets them without querying the warehouse.
Fresh analytics without loading windows
Because changes stream continuously, analysts query current data instead of waiting for last night's load.
Offload heavy reads
Point analytical queries at the synced copy in Databricks and keep MongoDB focused on its operational workload.
What you can sync between Databricks and MongoDB
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 | MongoDB objects | |
|---|---|---|
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Databases Logical groupings of collections that scope a sync connection. | |
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | Collections The table-like sync unit; each collection maps to a table or object in the paired system. | |
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Documents BSON records created, updated, and deleted during syncs, keyed by _id. | |
| Views Curated read-only projections used as sync sources for downstream tools. | Embedded documents and arrays Nested structures that syncs flatten or map to related records in relational targets. | |
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | Indexes Keep lookups by sync key fast on large collections. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Views Read-only aggregation-defined sources for filtered sync datasets. |
What ships with Databricks ⇄ MongoDB
Connect Databricks and MongoDB for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–MongoDB connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or MongoDB 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 MongoDB record.
Observability
Monitoring
Track your Databricks ⇄ MongoDB 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 MongoDB.
How the Databricks and MongoDB 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
MongoDB
- Integration surface
- MongoDB wire protocol via official drivers; Atlas additionally offers an administration REST API for cluster management
- Authentication
- Database credentials (username/password) or TLS/SSL X.509 certificate (.pem upload), entered individually or via a MongoDB connection string (SRV or standard); Stacksync IP allowlisting required
- Change detection
- MongoDB oplog and change streams (requires the database to run as a replica set — even single-node); Stacksync leverages these built-in tools to track changes in real time
- Capabilities
- read · write · CDC
How it works
How to connect Databricks to MongoDB — 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 MongoDB 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
MongoDB
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and MongoDB 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 ⇄ MongoDBCustomers 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 MongoDBCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and MongoDB integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Databricks and MongoDB: 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.
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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. MongoDB: MongoDB wire protocol via official drivers; Atlas additionally offers an administration REST API for cluster management. Authentication: Database credentials (username/password) or TLS/SSL X.509 certificate (.pem upload), entered individually or via a MongoDB connection string (SRV or standard); Stacksync IP allowlisting required. Stacksync manages authentication, retries, and rate limits on both sides.
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Databricks: Delta Lake's Change Data Feed records row-level inserts, updates, and deletes, enabling incremental sync without full scans. MongoDB: Change streams expose ordered change events with resume tokens, so an interrupted sync can pick up exactly where it stopped without a full re-read. Stacksync's field mapping accounts for these differences between Databricks and MongoDB 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 MongoDB 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 MongoDB connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Databricks–MongoDB integration in-house.
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Yes — Stacksync ships production-grade connectors for both Databricks and MongoDB. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
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 MongoDB.
Popular · 8 of 386
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Databricks ⇄ RavenDB -
Databricks ⇄ Redis Enterprise -
Databricks ⇄ SAP ASE (Sybase) -
Databricks ⇄ Scaleway Postgres -
MongoDB ⇄ Apache Druid -
MongoDB ⇄ Apache Hive -
MongoDB ⇄ Apache Impala -
MongoDB ⇄ Apache Kylin
No integrations match .
