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Data warehouse ⇄ CRM

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

Keep BigQuery and Chorusai 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 BigQuery and Chorusai

Sync Chorusai into BigQuery 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 BigQuery as live tables, updated within seconds, and columns computed in BigQuery 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 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources
  • 04 Feed ML feature tables in BigQuery from operational systems on a continuous schedule

Common sync patterns

Scores and segments back on the record

Lead scores, churn risk, or usage segments computed in BigQuery 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 BigQuery to build the customer picture the CRM alone cannot hold.

Cleanup that sticks

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

What you can sync between BigQuery and Chorusai

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.

BigQuery objects Chorusai objects How this pairing syncs
Partitioned tables Synced like regular tables; partition columns map to target fields. Playlists Curated collections of Moments and Recordings; created and managed to share coaching examples across teams. Partitioned tables is specific to BigQuery and Playlists to Chorusai — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Engagements Meetings and dialer calls, the core record; filterable by date_time, participants, and processing_state, and polled incrementally to read conversation activity out. Clustered tables is specific to BigQuery and Engagements to Chorusai — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. Recordings (Conversations) The recorded call with utterances, transcript, thumbnails, and metrics; read for analysis and uploaded or deleted through the API. Datasets is specific to BigQuery and Recordings (Conversations) to Chorusai — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Users Chorus users with roles and team membership; read to map engagement owners and participants to CRM and warehouse identities. Projects is specific to BigQuery and Users to Chorusai — each maps to any object or custom field on the other side.
Tables The syncable unit: only tables can be synced per the Stacksync docs. Trackers AI keyword and topic trackers (pricing, competitors, next steps) surfaced within a conversation; read out as coaching and deal-risk signals. Tables is specific to BigQuery and Trackers to Chorusai — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Chorusai

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.

BigQuery Chorusai Sub-second propagation

DetectionChanges in BigQuery are captured at the source via change data capture — no polling loop against its API. Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen").

DeliveryEach detected change is written to Chorusai through its API, with automatic retries and rate-limit backoff.

Chorusai BigQuery 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 BigQuery as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • BigQuery: Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes.
  • Chorusai: Rate limits are not publicly documented; recording upload and analysis are asynchronous, so transcripts and trackers appear only after processing_state completes.
What ships with BigQuery ⇄ Chorusai

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Chorusai 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 BigQuery or Chorusai record.

Observability

Monitoring

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

Trading partners

EDI

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

How the BigQuery and Chorusai connectors work

BigQuery

Integration surface
GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication
Google Cloud service account: create a dedicated service account, grant roles (BigQuery Data Editor, BigQuery Job User, Cloud Functions Service Agent, Cloud Run Developer, Eventarc Event Receiver
Change detection
Real-time notification service deployed into your Google Cloud project: Eventarc ("a notification service that enables real-time updates to happen") with a Cloud Run "secure portal for real-time notification service in
Capabilities
read · write · CDC
Rate limits
Subject to Google Cloud quotas on queries, DML, and streaming; DML is supported but the platform favors append-heavy batch and streaming loads over row-at-a-time writes
BigQuery setup guide

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
How it works

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

    Choose tables

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

BigQuery and Chorusai 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 563 integrations available for BigQuery and Chorusai.

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