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Data warehouse ⇄ CRM

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

Keep Databricks and Kommo 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 Kommo

Sync Kommo 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. Notes, Custom Fields, Users, Chats from Kommo land in Databricks as live tables, updated within seconds, and columns computed in Databricks write back to fields in Kommo. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Route leads that originate in WhatsApp or Instagram conversations into other systems of record
  • 02 Replicate Kommo leads and contacts into a database or warehouse for reporting beyond built-in dashboards
  • 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

CRM analytics on live data

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

Scores and segments back on the record

Lead scores, churn risk, or usage segments computed in Databricks appear as fields in Kommo, where the people working accounts actually see them.

A single customer view

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

What you can sync between Databricks and Kommo

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 Kommo objects How this pairing syncs
SQL Warehouses The compute endpoint a sync connects to for query execution. Contacts Person records sync with other CRMs and databases for a shared contact file. SQL Warehouses is specific to Databricks and Contacts to Kommo — each maps to any object or custom field on the other side.
Change Data Feed Row-level change records on Delta tables that drive incremental reads. Companies Organization records map to accounts in ERPs and invoicing tools. Change Data Feed is specific to Databricks and Companies to Kommo — each maps to any object or custom field on the other side.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Pipelines & Statuses Stage definitions structure lead progress and drive stage-change syncs to reporting tools. Catalogs is specific to Databricks and Pipelines & Statuses to Kommo — each maps to any object or custom field on the other side.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. Tasks Follow-up records keep rep activity consistent across systems. Schemas is specific to Databricks and Tasks to Kommo — 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. Notes Free-text and system notes attach context to synced leads and contacts. Delta Tables is specific to Databricks and Notes to Kommo — each maps to any object or custom field on the other side.
Views Curated read-only projections used as sync sources for downstream tools. Custom Fields Per-entity custom fields hold data written from external databases and enrichment. Views is specific to Databricks and Custom Fields to Kommo — each maps to any object or custom field on the other side.

How changes propagate between Databricks and Kommo

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

Kommo Databricks Sub-second propagation

DetectionKommo notifies Stacksync of record changes through webhook events. Webhooks on record add and update events, plus polling for backfill.

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.
  • Kommo: Subject to per-account request rate limits.
What ships with Databricks ⇄ Kommo

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks or Kommo 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 Kommo record.

Observability

Monitoring

Track your Databricks ⇄ Kommo 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 Kommo.

How the Databricks and Kommo 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

Kommo

Integration surface
REST API
Authentication
OAuth 2.0 with refresh tokens
Change detection
Webhooks on record add and update events, plus polling for backfill
Capabilities
read · write · webhooks
Rate limits
Subject to per-account request rate limits
How it works

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

    Choose tables

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

Databricks and Kommo 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 562 integrations available for Databricks and Kommo.

Popular · 7 of 562
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