Real-time
Two-way sync
Changes in Couchbase or Databricks instantly reflect in both systems. No stale data, no manual imports.
Database ⇄ Data warehouse
Couchbase to Databricks integration — real-time, two-way sync
Keep Couchbase and Databricks 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
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Migrated from Mulesoft
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Migrated from Celigo
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Migrated from Matillion
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Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Couchbase and Databricks
Connect Couchbase 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 Couchbase'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 Couchbase where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Couchbase sync into Databricks in real time, and result tables in Databricks sync back into Couchbase, with schema and type mapping between the two systems handled for you.
Common use cases
- Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
- Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
- Two-way sync between Couchbase (serving application data) and a CRM so support and sales teams see live application state.
- Consolidate documents from multiple Couchbase clusters into a single analytical store.
Offload heavy reads
Point analytical queries at the synced copy in Databricks and keep Couchbase focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Couchbase land in Databricks as they change, replacing hand-built CDC and batch extract jobs.
Serve warehouse results at database speed
Aggregates or model outputs computed in Databricks sync into Couchbase, where whatever reads from that database gets them without querying the warehouse.
What you can sync between Couchbase and Databricks
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.
| Couchbase objects | Databricks objects | |
|---|---|---|
| XDCR replications Cluster-to-cluster replication streams, useful context when choosing a sync source cluster. | Volumes Unity Catalog file storage used for staging bulk loads. | |
| Full-text search indexes Search indexes over documents, relevant when syncing searchable content. | SQL Warehouses The compute endpoint a sync connects to for query execution. | |
| Buckets Top-level data containers, roughly analogous to a database, that scope replication and memory quotas. | Change Data Feed Row-level change records on Delta tables that drive incremental reads. | |
| Scopes Namespaces inside a bucket used to group collections, similar to schemas. | Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | |
| Collections Table-like groupings of documents that syncs typically map one-to-one to destination tables. | Schemas Group tables and views; syncs typically target a dedicated schema per source system. | |
| JSON Documents The core records; schemaless JSON keyed by document id, flattened or mapped to relational rows in syncs. | Delta Tables The primary read and write target; operational data lands here as managed or external tables. |
What ships with Couchbase ⇄ Databricks
Connect Couchbase and Databricks for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Couchbase–Databricks connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Couchbase or Databricks 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 Couchbase or Databricks record.
Observability
Monitoring
Track your Couchbase ⇄ Databricks sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Couchbase and Databricks.
How the Couchbase and Databricks connectors work
Couchbase
- Integration surface
- SQL++ (N1QL) query service, key-value SDK APIs, and REST management APIs
- Authentication
- Database credentials with role-based access control, typically over TLS
- Change detection
- Database Change Protocol (DCP) streams document mutations; SQL++ polling on document fields as an alternative
- Capabilities
- read · write · CDC
- Rate limits
- Throughput is bounded by cluster sizing rather than API rate limits
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
How it works
How to connect Couchbase to Databricks — 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 Couchbase and Databricks with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Couchbase
connected
Databricks
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Couchbase and Databricks 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 · Couchbase ⇄ DatabricksCustomers 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.
Couchbase DatabricksCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Couchbase and Databricks integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Couchbase and Databricks: authenticate both systems, choose the objects to sync (such as Couchbase's XDCR replications and Full-text search indexes), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Common patterns for Couchbase and Databricks: Offload heavy reads; Operational data in the warehouse, minus the pipeline; Serve warehouse results at database speed. Point analytical queries at the synced copy in Databricks and keep Couchbase focused on its operational workload.
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Couchbase: SQL++ (N1QL) query service, key-value SDK APIs, and REST management APIs. Authentication: Database credentials with role-based access control, typically over TLS. 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. Stacksync manages authentication, retries, and rate limits on both sides.
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Databricks: Unity Catalog imposes a three-level namespace (catalog.schema.table) that governs access across workspaces. Couchbase: SQL++ (N1QL) provides SQL-style querying over JSON documents, including joins across collections, so extraction can be query-shaped rather than key-by-key. Stacksync's field mapping accounts for these differences between Couchbase and Databricks 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 Couchbase and Databricks 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 Couchbase and Databricks connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Couchbase–Databricks integration in-house.
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 Couchbase and Databricks.
Popular · 8 of 386
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Couchbase ⇄ ClickHouse -
Couchbase ⇄ Cloudera Data Platform -
Couchbase ⇄ Dremio -
Couchbase ⇄ Exasol -
Databricks ⇄ Amazon Aurora -
Databricks ⇄ Amazon Lightsail -
Databricks ⇄ Apache Cassandra -
Databricks ⇄ Azure Cosmos DB
No integrations match .
