Real-time
Two-way sync
Changes in Databricks or Starburst Enterprise instantly reflect in both systems. No stale data, no manual imports.
Data warehouse
Databricks to Starburst Enterprise integration — real-time, two-way sync
Keep Databricks and Starburst Enterprise 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 Starburst Enterprise
Keep tables consistent across Databricks and Starburst Enterprise, for a migration, a multi-warehouse stack, or a dataset two platforms both need.
Companies end up with two warehouses for practical reasons: a migration in progress, teams that standardized on different platforms, an acquisition, or tools that only connect to one of them. The result is the same dataset maintained twice, with duplicated pipelines and numbers that almost match.
Stacksync syncs tables between Databricks and Starburst Enterprise continuously, in either or both directions. Rows changed on one platform appear on the other within seconds, with schema and type mapping handled, so both warehouses answer questions with the same data.
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.
- Read federated views that join warehouse, lake, and database tables, then sync the result into operational tools like a CRM
- Expose Iceberg or Hive lake tables to sync jobs through one SQL endpoint without moving the data first
Migration without a big bang
When one platform is replacing the other, keep tables mirrored while workloads move over gradually, and cut over with nothing to backfill.
Serve tools that only connect to one platform
Mirror the datasets a BI tool, notebook, or application needs onto the platform it can actually reach.
Shared datasets across teams
Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.
What you can sync between Databricks and Starburst Enterprise
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 | Starburst Enterprise objects | |
|---|---|---|
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | Tables Queryable relations; writes pass through to sources whose connectors support them. | |
| Volumes Unity Catalog file storage used for staging bulk loads. | Views Engine-level SQL views used to shape federated data before syncing it out. | |
| SQL Warehouses The compute endpoint a sync connects to for query execution. | Materialized views Precomputed results that make repeated sync reads cheaper. | |
| Change Data Feed Row-level change records on Delta tables that drive incremental reads. | Columns Typed per the Trino type system, mapped from each source's native types. | |
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Catalogs Each catalog maps to a connector (Iceberg, Hive, PostgreSQL, and others) exposing an external source. | |
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | Schemas Namespaces within a catalog, mirroring the underlying source's databases or schemas. |
What ships with Databricks ⇄ Starburst Enterprise
Connect Databricks and Starburst Enterprise for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Starburst Enterprise connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Starburst Enterprise 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 Starburst Enterprise record.
Observability
Monitoring
Track your Databricks ⇄ Starburst Enterprise 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 Starburst Enterprise.
How the Databricks and Starburst Enterprise 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
Starburst Enterprise
- Integration surface
- ANSI SQL over JDBC/ODBC drivers and the Trino client REST protocol
- Authentication
- Deployment-dependent: username/password, LDAP, OAuth 2.0, or Kerberos
- Change detection
- Query-based polling; Starburst is a query engine and exposes no change log of its own
- Capabilities
- read · write
- Rate limits
- Throughput is governed by cluster sizing and resource groups rather than API quotas
How it works
How to connect Databricks to Starburst Enterprise — 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 Starburst Enterprise 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
Starburst Enterprise
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Starburst Enterprise 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 ⇄ Starburst EnterpriseCustomers 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 Starburst EnterpriseCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Starburst Enterprise integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Starburst Enterprise: authenticate both systems, choose the objects to sync (such as Databricks's Materialized Views and Volumes), map fields visually, and changes propagate both ways in milliseconds — no code required.
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Stacksync pricing is usage-based and starts at $1,000/month, including the managed Databricks and Starburst Enterprise connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Databricks–Starburst Enterprise integration in-house.
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Yes — Stacksync ships production-grade connectors for both Databricks and Starburst Enterprise. 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 Starburst Enterprise: Query-based polling; Starburst is a query engine and exposes no change log of its own. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
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On the Databricks side: Delta Tables, Views, Materialized Views, Volumes, plus custom fields where Databricks exposes them. On the Starburst Enterprise side: Materialized views, Columns, Catalogs, Schemas. Stacksync auto-detects both schemas and converts types between the two systems.
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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.
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 Starburst Enterprise.
Popular · 8 of 386
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Databricks ⇄ MotherDuck -
Databricks ⇄ AWS S3 -
Databricks ⇄ Actian Vector -
Databricks ⇄ Amazon Redshift -
Starburst Enterprise ⇄ Snowflake -
Starburst Enterprise ⇄ BigQuery - Starburst Enterprise ⇄ MotherDuck
- Starburst Enterprise ⇄ AWS S3
No integrations match .
