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Data warehouse

Databricks to Firebolt integration — real-time, two-way sync

Keep Databricks and Firebolt 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
Migrated from Heroku Connect
Migrated from Matillion
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Migrated from Fivetran
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Migrated from Celigo
Why teams connect Databricks and Firebolt

Keep tables consistent across Databricks and Firebolt, 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 Firebolt 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.
  • Keep dimension tables aligned with source systems while high-volume event data loads through separate batch pipelines.
  • Sync aggregated results from Firebolt back to operational tools that need computed metrics (reverse ETL).

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 Firebolt

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 Firebolt objects
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Aggregating indexes Precomputed rollups maintained at write time; incremental loads update them automatically.
Volumes Unity Catalog file storage used for staging bulk loads. Engines Compute resources that must be running for a sync to read or write.
SQL Warehouses The compute endpoint a sync connects to for query execution. Databases Logical containers holding the tables a sync targets.
Change Data Feed Row-level change records on Delta tables that drive incremental reads. Tables Managed columnar tables written with SQL; the main sync destination.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. External tables References to files in object storage used to stage bulk loads.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Views Curated query surfaces commonly used as sources for reverse ETL.
What ships with Databricks ⇄ Firebolt

Connect Databricks and Firebolt for flexible, real-time data sync.

Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Databricks–Firebolt connection.

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Firebolt 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 Firebolt record.

Observability

Monitoring

Track your Databricks ⇄ Firebolt 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 Firebolt.

How the Databricks and Firebolt 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

Firebolt

Integration surface
SQL over a REST API, with JDBC, Python, and Node.js SDKs
Authentication
Service account credentials (client ID and secret) exchanged for OAuth 2.0 tokens
Change detection
Polling; Firebolt is an analytics destination and does not expose a change feed
Capabilities
read · write
Rate limits
No fixed request quota; throughput depends on the engine size attached to the workload
How it works

How to connect Databricks to Firebolt — 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 Firebolt 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
    Firebolt connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Databricks and Firebolt 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 ⇄ Firebolt
    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 Firebolt
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Databricks and Firebolt integration FAQ

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.

Learn more about security
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:

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 386 integrations available for Databricks and Firebolt.

Popular · 8 of 386
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