Reverse ETL to 150+ tools
Push modeled metrics, scores, and audiences from Databricks into CRMs, ad platforms, and support tools, so teams act on warehouse data in the apps they already use.
Databricks
Data Warehouse
Activate and distribute Databricks data across your stack.
Dataddo turns Databricks into the source of truth your whole stack runs on. Activate modeled metrics and audiences into 150+ business tools with reverse ETL, fan data out to other warehouses, databases, and lakes, and deliver governed extracts to partners - all fully managed, with the data plane running where you choose. No pipelines to build, no lock-in.
ARCHITECTURE
Where Databricks fits as a source in your data stack
Sources
Business / DB / File / Streaming Connectors
450+ available, any direction
HubSpot
Google Ads
Google Sheets
Salesforce
SAP S/4HANA
Oracle
Microsoft Dynamics 365
AWS S3
Azure SQL
Google Cloud SQL
MySQL
PostgreSQL
IBM Db2
+ any other
Your existing stack
Orchestration
Monitoring
Governance & Lineage
IAM & SSO
Dataddo Platform
Speed
Security
Governance
Control Plane
UI
Visual workspace for teams to build, run and monitor pipelines
API
Programmatic interface to embed Dataddo in your own stack and workflows
MCP
Dedicated interface for AI & agents to access governed data in context
Data Plane
Isolated deployment
Hyperscalers
AWSAzureGoogle Cloud
Isolated deployment
EU Cloud Providers
HetznerOVHcloudSTACKIT
Isolated deployment
On-Prem
KubernetesOpenShift
Data + Metadata
Destinations
DWH / Data Lake / Lakehouse
Snowflake
Databricks
Microsoft Fabric
Google BigQuery
Amazon Redshift
Azure Synapse
Oracle
IBM Db2
Universal PostgreSQL
Universal MySQL
+ any other
Data + Metadata
Consumption
AI & Agents / Analytics
Claude
Gemini
OpenAI
Mistral
BottleCap AI
Power BI
Tableau
Looker Studio
Databox
+ any other
Sources
Business / DB / File / Streaming Connectors
450+ available, any direction
Orchestration
Monitoring
Governance & Lineage
IAM & SSO
Dataddo Platform
Speed
Security
Governance
Control Plane
Data Plane
Destinations
DWH / Data Lake / Lakehouse
Consumption
AI & Agents / Analytics
ETLELTReverse ETLCDCData Streaming
Any direction, any workload
YOUR SOURCE OF TRUTH
Turn Databricks into the source your whole stack runs on
The warehouse is where your data is modeled and governed. Dataddo serves it to every downstream system - operational tools, other databases, lakes, and partners - continuously and without manual exports.
Fan out to warehouses & databases
Replicate curated Databricks tables into other warehouses, operational databases, and read replicas - for cross-cloud analytics, migrations, or app back-ends.
Deliver to lakes, files & partners
Export Databricks data to object storage and lakehouse formats (Parquet, CSV, JSON), or send scheduled extracts to partners over SFTP.
Model once, serve everywhere
Blend and reshape Databricks data, then let the Data Quality Firewall stop bad records and automatic PII detection mask sensitive fields before it leaves the warehouse.
Incremental, on your schedule
Incremental syncs move only new or changed rows on the schedule you set, so downstream systems stay fresh without full reloads.
Governed and consistent
One governed definition in Databricks, distributed consistently to every channel - with delivery alerts and quality checks catching gaps early.
WITH VS. WITHOUT
Who carries the load when things change
Keep Databricks data flowing to every downstream tool and system - and see who owns it when an API, schema, or destination changes:
| Without Dataddo |
With Dataddo
|
Outcome for you | |
|---|---|---|---|
| API or auth change | You discover the breakage and scramble to fix it. | We update the connector and restore the pipeline - often before you notice. | Syncs from Databricks keep flowing |
| Schema drift | Columns change and pipelines break or corrupt data silently. | Detected automatically and handled by configurable rules. | Only clean Databricks data lands downstream |
| Endpoint deprecated | You re-engineer the integration. | We own the update - the data contract holds. | Your Databricks pipelines keep working |
| Missing connector | You build and maintain a custom integration. | We build it and maintain it, under a ~4-week SLA. | Databricks can reach any destination |
| Silent degradation | You find out when a report or model run fails. | Proactive monitoring catches anomalies and delays first. | Issues caught before downstream consumers break |
| Debugging | You dig through logs across disconnected tools. | Run histories, payload inspection, and end-to-end lineage in one place. | Faster root-cause, less downtime |
Data residency by design
Choose where Databricks data is processed - same cloud, region, or sovereign
Databricks already lives in the cloud, so the real question isn't on-prem vs cloud - it's which cloud and region. With Dataddo you run the data plane in the same cloud and region as Databricks, so reads stay in-region with low egress and latency, and payload flows straight from Databricks to your destinations without transiting a third-party SaaS. Pin it to a sovereign region for compliance, or run it on-premises when a downstream target lives inside your own network.
| Data Plane location | Typical data sensitivity | Why this setup |
|---|---|---|
|
Public cloud
AWS
Microsoft Azure
Google Cloud
|
Low to moderate - general business, marketing, and product data; sources that are already cloud-native. | Fastest to stand up and scales elastically. Best when the data has no residency restriction and often already lives in the same public cloud. |
|
Regional & EU sovereign clouds
EU sovereign clouds
Regional providers
Private cloud
|
Regulated / residency-bound - PII, financial, and health data governed by GDPR or local law. | Keeps processing inside a specific jurisdiction to meet data-residency and sovereignty rules, while still running as managed infrastructure. |
|
On-premises 
Kubernetes
Red Hat OpenShift
VMware Tanzu
|
Highly sensitive / restricted - data that contractually or legally cannot leave the corporate perimeter. | Data never leaves your network. Required for air-gapped, classified, or locked-down environments; the Control Plane still manages it via metadata only. |
DATA TRANSPORT
Every way to serve Databricks data to other systems
A warehouse is a batch source, not a change stream. Dataddo activates Databricks data into business apps, fans it out to other databases and warehouses, and delivers files to storage and partners - all governed the same way.
| Transport type | What it does | Typical destinations | Typical business use cases |
|---|---|---|---|
| Reverse ETL | Activate your data: push curated, governed records from your AI agents or warehouse back into CRMs, operational systems, and the frontier apps where your teams act on it. | Activate modeled metrics, scores, and segments from Databricks in CRMs, ad platforms, and support tools, Sync audiences from Databricks to ad platforms for targeting, suppression, and lookalikes | |
| ETL & ELT | Classic extract-transform-load, or load-first with in-warehouse transformation. | Fan Databricks data out to other warehouses or operational databases for cross-cloud analytics and app back-ends, Replicate curated Databricks tables to a sovereign region or a partner environment | |
| Batch File Delivery | Structured delivery of datasets via files to S3, SFTP, or any storage target. | Export Databricks tables to S3, GCS, or Azure storage as Parquet, CSV, or JSON, Deliver scheduled Databricks extracts to partners and clients over SFTP |
Meta
Google Cloud Storage
Azure Blob Storage
SFTP
Case Studies
See what data teams build with Dataddo
65,000 Social Media Accounts. One Platform. Zero Manual Authorizations.
Beauty & Consumer Goods
How Livesport Activates Data, Saves Engineering Resources with BigQuery and Dataddo
Entertainment
How ID&T Group Activates Data from 1M+ Festival Fans and Dozens of Social Accounts
Entertainment
How Sensire Accelerated Migration of a Proprietary On-Premise Data Infrastructure to the Cloud with Dataddo
Healthcare
How Ringside.ai Builds a Data Product Better and Faster Using Dataddo
Marketing
How Publicis Groupe Brasil Uses Dataddo's API to Scale a Data Product
Advertising
FAQ
Databricks as a source + Dataddo, answered
Where can I send Databricks data?
To any of Dataddo's 150+ destinations - CRMs like Salesforce and HubSpot, ad platforms like Google Ads and Meta, other warehouses and operational databases, object storage and data lakes, BI tools, and AI or vector stores - plus custom destinations on request.
Do I have to maintain the pipelines?
No. Dataddo is fully managed: we maintain the connectors, adapt to schema changes in Databricks, and alert you if anything needs attention. Run it fully in the cloud with nothing to operate, or self-host the data plane as a lightweight agent - either way there is no pipeline code for you to maintain.
Is this reverse ETL?
Yes - reverse ETL is the primary pattern. Dataddo reads modeled tables from Databricks and pushes them into the operational tools your teams use, with insert, update, and upsert writes. It also fans Databricks data out to other databases, warehouses, lakes, and files.
How is my data secured?
Dataddo is SOC 2 Type II and ISO 27001 certified. Data is encrypted in transit and at rest, PII can be masked or hashed, and if you run the data plane on-premises, payload data never leaves your network. EU or US data residency is available.
Will this add load to Databricks?
Dataddo reads Databricks with incremental, scheduled queries that select only new or changed rows, so you move only the data you need and keep query cost and warehouse load predictable.
Am I locked in?
No. Pipelines are warehouse- and destination-agnostic - you can add or switch sources and destinations without rebuilding, and data lands in native formats you own.