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CRM ⇄ Data warehouse

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

Keep Chorusai 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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Why teams connect Chorusai and Databricks

Sync Chorusai 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. Engagements, Recordings (Conversations), Users, Trackers from Chorusai land in Databricks as live tables, updated within seconds, and columns computed in Databricks write back to fields in Chorusai. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Sync Trackers such as pricing, competitor, and next-steps mentions onto matching CRM Opportunities as coaching and deal-risk signals.
  • 02 Export Scorecards and their scores to a database to power rep-coaching and QA dashboards across sales teams.
  • 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 Chorusai 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 Chorusai, where the people working accounts actually see them.

A single customer view

Join Chorusai'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 Chorusai 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.

Chorusai objects Databricks objects How this pairing syncs
Engagements Meetings and dialer calls, the core record; filterable by date_time, participants, and processing_state, and polled incrementally to read conversation activity out. Materialized Views Precomputed results read on a schedule for reverse-ETL style syncs. Engagements is specific to Chorusai and Materialized Views to Databricks — each maps to any object or custom field on the other side.
Recordings (Conversations) The recorded call with utterances, transcript, thumbnails, and metrics; read for analysis and uploaded or deleted through the API. Volumes Unity Catalog file storage used for staging bulk loads. Recordings (Conversations) is specific to Chorusai and Volumes to Databricks — each maps to any object or custom field on the other side.
Users Chorus users with roles and team membership; read to map engagement owners and participants to CRM and warehouse identities. SQL Warehouses The compute endpoint a sync connects to for query execution. Users is specific to Chorusai and SQL Warehouses to Databricks — each maps to any object or custom field on the other side.
Trackers AI keyword and topic trackers (pricing, competitors, next steps) surfaced within a conversation; read out as coaching and deal-risk signals. Change Data Feed Row-level change records on Delta tables that drive incremental reads. Trackers is specific to Chorusai and Change Data Feed to Databricks — each maps to any object or custom field on the other side.
Deals CRM opportunity context attached to a conversation (account, deal, owner); read to attribute call activity to open pipeline. Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. Deals is specific to Chorusai and Catalogs to Databricks — each maps to any object or custom field on the other side.
Scorecards Call QA and coaching assessments with reviewer, recipient, and scores; read and exported for rep-performance reporting. Schemas Group tables and views; syncs typically target a dedicated schema per source system. Scorecards is specific to Chorusai and Schemas to Databricks — each maps to any object or custom field on the other side.

How changes propagate between Chorusai 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.

Chorusai Databricks Interval-based propagation

DetectionStacksync polls Chorusai for changes on an incremental schedule, reading only records changed since the previous pass. Polling the engagements endpoint on date_time and processing_state.

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

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

Rate-limit considerations

  • Chorusai: Rate limits are not publicly documented; recording upload and analysis are asynchronous, so transcripts and trackers appear only after processing_state completes.
  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
What ships with Chorusai ⇄ Databricks

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Chorusai 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 Chorusai or Databricks record.

Observability

Monitoring

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

Trading partners

EDI

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

How the Chorusai and Databricks connectors work

Chorusai

Integration surface
REST API (api-docs.chorus.ai)
Authentication
Per-user API token generated in Chorus Personal Settings, sent in the Authorization request header
Change detection
Polling the engagements endpoint on date_time and processing_state; no public change webhooks or CDC
Capabilities
read · write
Rate limits
Rate limits are not publicly documented; recording upload and analysis are asynchronous, so transcripts and trackers appear only after processing_state completes
Chorusai 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 Chorusai 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 Chorusai 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
    Chorusai connected
    Databricks connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Chorusai 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 571 integrations available for Chorusai and Databricks.

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