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Data warehouse

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

Keep Databricks and Dremio 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
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Migrated from Fivetran
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Migrated from Celigo
Why teams connect Databricks and Dremio

Keep tables consistent across Databricks and Dremio, 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 Dremio 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

  • 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.
  • Publish operational database tables into Iceberg via Dremio so the lakehouse reflects current application state.
  • Consolidate data from multiple lake sources through one Dremio semantic layer into a single warehouse target.

Shared datasets across teams

Where different teams run different warehouses, sync the curated tables both rely on so their metrics agree by construction.

Consolidation after M&A

Bring the acquired company's warehouse data across continuously instead of through one-off dumps.

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.

What you can sync between Databricks and Dremio

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 Dremio objects
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Physical datasets Tables and files promoted from sources; the raw data a sync ultimately reads.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. Virtual datasets (views) SQL views layering semantics over physical data; the preferred sync target for curated extracts.
Views Curated read-only projections used as sync sources for downstream tools. Apache Iceberg tables Lakehouse tables supporting DML and snapshot metadata usable for incremental reads.
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Spaces and folders Namespaces that organize virtual datasets and govern access.
Volumes Unity Catalog file storage used for staging bulk loads. Reflections Materialized accelerations that make repeated extraction queries cheaper.
SQL Warehouses The compute endpoint a sync connects to for query execution. Jobs Query execution records useful for monitoring sync workloads.
What ships with Databricks ⇄ Dremio

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Dremio 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 Dremio record.

Observability

Monitoring

Track your Databricks ⇄ Dremio 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 Dremio.

How the Databricks and Dremio 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

Dremio

Integration surface
Arrow Flight SQL, JDBC/ODBC, and a REST API
Authentication
Personal access tokens or username/password; OAuth-based SSO on Dremio Cloud
Change detection
Polling via SQL; Iceberg table snapshots can anchor incremental reads; no consumer-facing change feed
Capabilities
read · write
Rate limits
Bounded by engine capacity and workload management rather than API rate limits
How it works

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

    Choose tables

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

Databricks and Dremio 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 Dremio.

Popular · 8 of 386
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