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Data warehouse ⇄ CRM

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

Keep BigQuery and DealCloud 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 BigQuery and DealCloud

Sync DealCloud 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. Task, User, Deal, Company from DealCloud land in BigQuery as live tables, updated within seconds, and columns computed in BigQuery write back to fields in DealCloud. There is no separate ETL and reverse-ETL stack to stitch together and no jobs to babysit.

Common use cases

  • 01 Push portfolio and fund performance data from a warehouse into DealCloud so dealmakers see up-to-date metrics on each investment.
  • 02 Keep DealCloud contacts and relationships in sync with an enrichment or email platform to maintain accurate firm-wide relationship intelligence.
  • 03 Activate modeled BigQuery tables by syncing computed attributes back into sales and marketing tools
  • 04 Maintain a customer master table in BigQuery joined across CRM, billing, and support sources

Common sync patterns

A single customer view

Join DealCloud'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.

CRM analytics on live data

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

What you can sync between BigQuery and DealCloud

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 DealCloud objects How this pairing syncs
Tables The syncable unit: only tables can be synced per the Stacksync docs. Investment Synced with incremental and full sync. Tables is specific to BigQuery and Investment to DealCloud — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Relationship Synced with incremental and full sync. Partitioned tables is specific to BigQuery and Relationship to DealCloud — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Activity Synced with incremental and full sync. Clustered tables is specific to BigQuery and Activity to DealCloud — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. Task Synced with incremental and full sync. Datasets is specific to BigQuery and Task to DealCloud — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. User Synced with incremental and full sync. Projects is specific to BigQuery and User to DealCloud — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and DealCloud

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

DealCloud BigQuery Interval-based propagation

DetectionStacksync polls DealCloud for changes on an incremental schedule, reading only records changed since the previous pass. Incremental via each entry's last-modified timestamp.

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.
  • DealCloud: API request limits apply per firm tenant; Stacksync manages throttling and retries automatically.
What ships with BigQuery ⇄ DealCloud

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or DealCloud 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 DealCloud record.

Observability

Monitoring

Track your BigQuery ⇄ DealCloud 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 DealCloud.

How the BigQuery and DealCloud 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

DealCloud

Integration surface
REST API (DealCloud Data API v2)
Authentication
OAuth 2.0 client-credentials; a DealCloud administrator generates a client ID and secret in the DealCloud admin API settings and grants Stacksync the required scopes
Change detection
Incremental via each entry's last-modified timestamp; DealCloud has no universal native change-data-capture, so Stacksync polls modified rows on an interval
Capabilities
read · write
Rate limits
API request limits apply per firm tenant; Stacksync manages throttling and retries automatically.
How it works

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

    Choose tables

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

BigQuery and DealCloud 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 392 integrations available for BigQuery and DealCloud.

Popular · 8 of 392
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