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Data warehouse ⇄ CRM

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

Keep Databricks and Vitally 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 Databricks and Vitally

Sync Vitally 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. Custom Trait, Account, User, Organization from Vitally land in Databricks as live tables, updated within seconds, and columns computed in Databricks write back to fields in Vitally. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Keep CS tasks and notes aligned between Vitally and ticketing or project tools.
  • 02 Sync account health scores and lifecycle stages from Vitally into a CRM so sales sees churn risk before renewals.
  • 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

A single customer view

Join Vitally'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 Vitally are queryable in Databricks moments after they change, so dashboards stop lagging the reality they describe.

What you can sync between Databricks and Vitally

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 Vitally objects How this pairing syncs
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Account Core customer account records with health scores and lifecycle traits; created, updated, retrieved, and listed via the REST API. Schemas is specific to Databricks and Account to Vitally — each maps to any object or custom field on the other side.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. User End users tied to accounts, including activity and custom traits. Delta Tables is specific to Databricks and User to Vitally — each maps to any object or custom field on the other side.
Views Curated read-only projections used as sync sources for downstream tools. Organization Parent organizations for hierarchical B2B account structures. Views is specific to Databricks and Organization to Vitally — each maps to any object or custom field on the other side.
Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Task CS tasks and follow-ups, readable and writable for workflow sync. Materialized Views is specific to Databricks and Task to Vitally — each maps to any object or custom field on the other side.
Volumes Unity Catalog file storage used for staging bulk loads. Note Account and user notes captured by success teams. Volumes is specific to Databricks and Note to Vitally — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. Conversation Customer conversations logged in Vitally; activity objects include parent object details in the payload. SQL Warehouses is specific to Databricks and Conversation to Vitally — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Vitally

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.

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

Vitally Databricks Sub-second propagation

DetectionVitally notifies Stacksync of record changes through webhook events. Incremental polling on updatedAt cursors.

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

Rate-limit considerations

  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
  • Vitally: Default rate limit of 1,000 requests/min (token bucket); write operations consume more budget, headers expose remaining quota.
What ships with Databricks ⇄ Vitally

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Vitally 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 Vitally record.

Observability

Monitoring

Track your Databricks ⇄ Vitally 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 Vitally.

How the Databricks and Vitally 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

Vitally

Integration surface
REST API with cursor-based pagination (sortable by createdAt/updatedAt)
Authentication
API key via Basic Auth; keys created in Settings -> Integrations -> REST API and individually revocable
Change detection
Incremental polling on updatedAt cursors; playbook-triggered webhooks can push events for near real-time updates
Capabilities
read · write · webhooks
Rate limits
Default rate limit of 1,000 requests/min (token bucket); write operations consume more budget, headers expose remaining quota.
How it works

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

    Choose tables

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

Databricks and Vitally 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 390 integrations available for Databricks and Vitally.

Popular · 8 of 390
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