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CRM ⇄ Data warehouse

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

Keep Attio and Databricks 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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Why teams connect Attio and Databricks

Sync Attio into Databricks continuously and push warehouse results back onto CRM records, one two-way connection instead of two pipelines.

The CRM feeds the warehouse and the warehouse should feed the CRM: relationship data flows one way, and computed scores, segments, and customer context flow back. Most teams build the first half as a batch pipeline and never quite get to the second.

Stacksync does both with one connection. Deals, Workspaces, Custom objects, People from Attio land in Databricks as live tables, updated within seconds, and columns computed in Databricks write back to fields in Attio. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Keep Attio aligned with a billing system or ERP on customer records and plan status.
  • 02 Mirror lists and list entries into a database for pipeline reporting beyond the in-app views.
  • 03 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • 04 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.

Common sync patterns

A single customer view

Join Attio's relationship data with billing, product, and support data in Databricks to build the customer picture the CRM alone cannot hold.

Cleanup that sticks

Deduplication and normalization done in Databricks can be written back, so warehouse-side cleanup actually fixes the CRM.

CRM analytics on live data

Accounts, contacts, and activity from Attio are queryable in Databricks moments after they change, so dashboards stop lagging the reality they describe.

What you can sync between Attio and Databricks

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.

Attio objects Databricks objects How this pairing syncs
Custom objects Workspace-defined objects that behave like standard ones in the API. Change Data Feed Row-level change records on Delta tables that drive incremental reads. Custom objects is specific to Attio and Change Data Feed to Databricks — each maps to any object or custom field on the other side.
People Standard person object; synced with marketing tools and warehouse person tables. Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. People is specific to Attio and Catalogs to Databricks — each maps to any object or custom field on the other side.
Companies Standard company object; matched to billing and product accounts in two-way syncs. Schemas Group tables and views; syncs typically target a dedicated schema per source system. Companies is specific to Attio and Schemas to Databricks — each maps to any object or custom field on the other side.
Users Synced with incremental and full sync per the Stacksync docs. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Users is specific to Attio and Delta Tables to Databricks — each maps to any object or custom field on the other side.
Deals Pipeline records; read out for revenue reporting and written to from automation. Views Curated read-only projections used as sync sources for downstream tools. Deals is specific to Attio and Views to Databricks — each maps to any object or custom field on the other side.
Workspaces Synced with incremental and full sync per the Stacksync docs. Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Workspaces is specific to Attio and Materialized Views to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Attio and Databricks

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.

Attio Databricks Sub-second propagation

DetectionAttio notifies Stacksync of record changes through webhook events. Webhooks on record and list-entry events, with polling as a fallback.

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

Databricks Attio 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 written to Attio through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Attio: Subject to the platform's published API rate limits.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Attio ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Attio or Databricks 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 Attio or Databricks record.

Observability

Monitoring

Track your Attio ⇄ Databricks sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Attio and Databricks.

How the Attio and Databricks connectors work

Attio

Integration surface
REST API
Authentication
Guided in-app connection ("Attio CRM" connection created in a few clicks, "without any coding required"); the docs do not name the underlying auth mechanism (OAuth vs API key)
Change detection
Webhooks on record and list-entry events, with polling as a fallback
Capabilities
read · write · webhooks
Rate limits
Subject to the platform's published API rate limits.
Attio setup guide

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
How it works

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

    Choose tables

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

Attio and Databricks 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
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 392 integrations available for Attio and Databricks.

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