Real-time
Two-way sync
Changes in Databricks or Supabase instantly reflect in both systems. No stale data, no manual imports.
Data warehouse ⇄ Database
Databricks to Supabase integration — real-time, two-way sync
Keep Databricks and Supabase 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 Supabase
Connect Supabase 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 Supabase'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 Supabase where the services that read from it get them at normal query latency.
Stacksync covers both directions with one connection. Tables or collections in Supabase sync into Databricks in real time, and result tables in Databricks sync back into Supabase, with schema and type mapping between the two systems handled for you.
Common use cases
- Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
- Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
- Reflect auth.users state into support and CRM systems so teams see account status without querying the database
- Push product events captured in Supabase Postgres to marketing tools for lifecycle campaigns
Offload heavy reads
Point analytical queries at the synced copy in Databricks and keep Supabase focused on its operational workload.
Operational data in the warehouse, minus the pipeline
Rows from Supabase 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 Supabase, where whatever reads from that database gets them without querying the warehouse.
What you can sync between Databricks and Supabase
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 | Supabase objects | |
|---|---|---|
| Change Data Feed Row-level change records on Delta tables that drive incremental reads. | auth.users Managed authentication users, often mirrored into CRM or support systems. | |
| Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. | Row Level Security Policies Row-level access rules that govern what the REST layer exposes. | |
| Schemas Group tables and views; syncs typically target a dedicated schema per source system. | JSONB Columns Semi-structured payloads such as event properties or nested objects. | |
| Delta Tables The primary read and write target; operational data lands here as managed or external tables. | Database Functions Postgres functions that can transform or validate synced rows. | |
| Views Curated read-only projections used as sync sources for downstream tools. | Storage Object Metadata File metadata rows that can be joined to synced application data. | |
| Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. | Tables Standard Postgres tables; the primary two-way sync target. |
What ships with Databricks ⇄ Supabase
Connect Databricks and Supabase for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Supabase connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Databricks or Supabase 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 Supabase record.
Observability
Monitoring
Track your Databricks ⇄ Supabase 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 Supabase.
How the Databricks and Supabase 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
Supabase
- Integration surface
- Direct PostgreSQL wire protocol connection, plus an auto-generated REST API (PostgREST)
- Authentication
- Database credentials (connection string) for SQL access; API keys (anon / service role) for the REST layer
- Change detection
- Log-based CDC via Postgres logical replication, the same WAL feed that powers Supabase Realtime; database webhooks can also fire on row changes
- Capabilities
- read · write · CDC · webhooks
- Rate limits
- SQL access is bounded by connection limits (pooled connections are provided); the REST layer is subject to the platform's limits
How it works
How to connect Databricks to Supabase — 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 Supabase 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
Supabase
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Databricks and Supabase 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 ⇄ SupabaseCustomers 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 SupabaseCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Databricks and Supabase integration FAQ
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Yes. Stacksync provides a managed, real-time two-way integration between Databricks and Supabase: authenticate both systems, choose the objects to sync (such as Databricks's Change Data Feed and Catalogs), map fields visually, and changes propagate both ways in milliseconds — no code required.
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On the Databricks side: SQL Warehouses, Change Data Feed, Catalogs, Schemas, plus custom fields where Databricks exposes them. On the Supabase side: Storage Object Metadata, Tables, Views, 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.
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Common patterns for Databricks and Supabase: 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 Supabase focused on its operational workload.
-
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. Supabase: Direct PostgreSQL wire protocol connection, plus an auto-generated REST API (PostgREST). Authentication: Database credentials (connection string) for SQL access; API keys (anon / service role) for the REST layer. 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. Supabase: Every Supabase project is a full PostgreSQL database, so standard Postgres drivers, SQL tooling, and log-based CDC apply directly. Stacksync's field mapping accounts for these differences between Databricks and Supabase without custom code.
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 Supabase.
Popular · 8 of 386
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Databricks ⇄ MySQL -
Databricks ⇄ MariaDB -
Databricks ⇄ SQL Server -
Databricks ⇄ Postgres Heroku -
Supabase ⇄ IBM Netezza -
Supabase ⇄ Materialize -
Supabase ⇄ Rockset -
Supabase ⇄ Starburst Enterprise
No integrations match .
