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AI ⇄ Data warehouse

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

Keep Autopilot 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.

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

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Why teams connect Autopilot and Databricks

Send the records Databricks holds into Autopilot for embedding, classification, and scoring, and land what Autopilot produces back in Databricks as new columns, one two-way connection instead of a batch job.

Databricks holds the raw records the business runs on; Autopilot turns those records into embeddings, scores, labels, and summaries. The two meet wherever a warehouse row needs to be enriched by a model and the result needs somewhere durable to live. Most teams stitch that meeting together with export scripts and a queue, then spend their time keeping the glue alive.

Stacksync syncs Lists, Custom Fields, Smart Segments, Journeys (Triggers) in Autopilot with Schemas, Delta Tables, Views, Materialized Views in Databricks field by field, in real time, and in both directions. Rows added or changed in Databricks flow into Autopilot as they happen, and the Lists, Custom Fields, Smart Segments, Journeys (Triggers) that Autopilot generates land back in Databricks as columns or tables, with field-level mapping and conflict rules in place of a custom pipeline.

The payoff is that model output stops living in a separate place from the data it describes. Once results sit in Databricks, they join against billing, product, and usage tables already there, so you can report on quality, cost, and coverage without moving anything by hand.

Common use cases

  • 01 Add contacts to a Journey from a database event or CRM stage change to start automated onboarding or nurture sequences.
  • 02 Mirror List and Smart Segment membership into a warehouse to power attribution and audience analytics alongside other sources.
  • 03 Use Change Data Feed to propagate only changed rows to downstream apps instead of full-table scans.
  • 04 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.

Common sync patterns

History that outlives a run

A continuously synced copy in Databricks preserves every generated result, so outputs stay auditable even as they are overwritten or expire inside Autopilot.

Feed live warehouse records to Autopilot

Rows added or changed in Databricks flow into Autopilot within seconds, so embeddings, classifications, and enrichments are computed on current data rather than a nightly extract.

Model output back in the warehouse

Scores, labels, embeddings, or summaries produced in Autopilot land in Databricks as columns or tables, queryable and joinable with the rest of the business data.

What you can sync between Autopilot 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.

Autopilot objects Databricks objects How this pairing syncs
Journeys (Triggers) Automation journeys; a contact can be added to a journey via its trigger endpoint to start automated email or SMS sequences. Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Journeys (Triggers) is specific to Autopilot and Catalogs to Databricks — each maps to any object or custom field on the other side.
Activities Per-contact activity and event history (opens, clicks, journey steps); read-only feed used for engagement reporting. Schemas Group tables and views; syncs typically target a dedicated schema per source system. Activities is specific to Autopilot and Schemas to Databricks — each maps to any object or custom field on the other side.
Contacts Core people records (email, name, custom fields, list and segment membership); upserted two-way as the primary sync object. Delta Tables The primary read and write target; operational data lands here as managed or external tables. Contacts is specific to Autopilot and Delta Tables to Databricks — each maps to any object or custom field on the other side.
Lists Static contact lists; membership is readable per list and writable by adding or removing contacts. Views Curated read-only projections used as sync sources for downstream tools. Lists is specific to Autopilot and Views to Databricks — each maps to any object or custom field on the other side.
Custom Fields User-defined contact properties (string, number, date, boolean); discovered so field keys map cleanly to destination columns. Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Custom Fields is specific to Autopilot and Materialized Views to Databricks — each maps to any object or custom field on the other side.
Smart Segments Rule-based dynamic audiences; membership is computed by Autopilot, so it is read-only over the API. Volumes Unity Catalog file storage used for staging bulk loads. Smart Segments is specific to Autopilot and Volumes to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Autopilot 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.

Autopilot Databricks Interval-based propagation

DetectionStacksync polls Autopilot for changes on an incremental schedule, reading only records changed since the previous pass. No CDC.

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

Databricks Autopilot 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 Autopilot through its API, with automatic retries and rate-limit backoff.

Rate-limit considerations

  • Autopilot: The REST API is capped at 100 requests/minute per account; exceeding it returns HTTP 429. Enterprise plans can request a higher limit. Contact writes accept an array for bulk upsert, and list and segment reads paginate via a bookmark cursor.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Autopilot ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Autopilot 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 Autopilot or Databricks record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Autopilot and Databricks connectors work

Autopilot

Integration surface
REST API (Autopilot v1); Autopilot rebranded to Ortto in 2021 and the newer Ortto API co-exists with the legacy Autopilot endpoints
Authentication
Per-account API key sent in the autopilotapikey request header (generated in account settings); requests use Content-Type application/json against https://api2.autopilothq.com/v1/
Change detection
No CDC; incremental sync polls the /contacts endpoint with bookmark cursor pagination and updated timestamps. Journey webhook actions can push specific contact events, but there is no general change-subscription webhook, so polling is the reliable path.
Capabilities
read · write
Rate limits
The REST API is capped at 100 requests/minute per account; exceeding it returns HTTP 429. Enterprise plans can request a higher limit. Contact writes accept an array for bulk upsert, and list and segment reads paginate via a bookmark cursor.
Autopilot 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 Autopilot 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 Autopilot 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
    Autopilot connected
    Databricks connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Autopilot 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.

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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 523 integrations available for Autopilot and Databricks.

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