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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.

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Adopted by fast-scaling companies moving mission-critical data in real time

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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

  • 01 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 02 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 03 Reflect auth.users state into support and CRM systems so teams see account status without querying the database
  • 04 Push product events captured in Supabase Postgres to marketing tools for lifecycle campaigns

Common sync patterns

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 How this pairing syncs
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Schemas Namespaces (public and custom) that scope sync access. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions. Custom fields on either side are included in the mapping.
Views Curated read-only projections used as sync sources for downstream tools. Views Read-side projections exposed to outbound syncs. Same entity on both sides — records pair one-to-one and field-level changes reconcile in both directions.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. JSONB Columns Semi-structured payloads such as event properties or nested objects. Delta Tables is specific to Databricks and JSONB Columns to Supabase — each maps to any object or custom field on the other side.
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Database Functions Postgres functions that can transform or validate synced rows. Materialized Views is specific to Databricks and Database Functions to Supabase — each maps to any object or custom field on the other side.
Volumes Unity Catalog file storage used for staging bulk loads. Storage Object Metadata File metadata rows that can be joined to synced application data. Volumes is specific to Databricks and Storage Object Metadata to Supabase — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. Tables Standard Postgres tables; the primary two-way sync target. SQL Warehouses is specific to Databricks and Tables to Supabase — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Supabase

Each direction of the sync is driven by what the source system can signal and what the destination accepts — detection, delivery, and expected latency below.

Databricks Supabase Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

DeliveryEach detected change is applied to Supabase as a row-level write, with types converted between the two schemas.

Supabase Databricks Sub-second propagation

DetectionSupabase pushes changes as they happen — webhook events backed by change data capture. Log-based CDC via Postgres logical replication, the same WAL feed that powers Supabase Realtime.

DeliveryEach detected change is applied to Databricks as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
  • Supabase: SQL access is bounded by connection limits (pooled connections are provided); the REST layer is subject to the platform's limits.
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.

Real-time

Two-way sync

Changes in Databricks or Supabase instantly reflect in both systems. No stale data, no manual imports.

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.

  1. 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.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Databricks connected
    Supabase connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Databricks ⇄ Supabase
    Customers 12,480
    Sales Orders 8,213
    Invoices 5,902
    Items 1,344
  3. 03

    Map fields

    Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.

    • Auto-map
    • Type casting
    • Transforms
    Databricks Supabase
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Databricks and Supabase integration FAQ

SECURITY

Security teams trust 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.

Learn more about security
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
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:

OAuth 2 SSH Tunnelling SSL certificates IP Whitelisting VPN gateway VPC peering and more

Related integrations

Every pair below is a real-time, two-way sync. Search all 584 integrations available for Databricks and Supabase.

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