Can you integrate BigQuery with Demandbase?

Explore a BigQuery and Demandbase integration with a Stacksync engineer. Stacksync support for Demandbase is not established by the connector documentation reviewed for this page. Start with one record and the update your business needs to identify an implementation path.

Can BigQuery and Demandbase sync in both directions?

Confirm two-way support with Stacksync for the records and fields you need in both systems. Access to a vendor API does not confirm that its Stacksync connector supports write-back.

What should be ready before the BigQuery and Demandbase initial load?

Prepare both accounts, the selected object schemas, stable source and destination IDs, and the expected outcome. Enable BigQuery, Cloud Run, Cloud Resource Manager, and Eventarc APIs in the target project. Identify the Demandbase account, edition, environment, and business objects the integration must access. Use the pair worksheet to record ownership and acceptance criteria.

How fast does the BigQuery to Demandbase sync run?

Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.

Will existing duplicates merge automatically?

No. Stacksync documents that pre-existing duplicates are not merged automatically when two-way sync begins. Review the initial dataset and matching plan before enabling it; an empty destination can simplify the first load.

How do I map BigQuery fields to Demandbase?

Start with one business entity and a stable record ID. Map a small set of editable fields with compatible types, test required values and relationships, then expand after the pilot passes.

What should I do if a record fails to sync?

Check the destination error, field constraints, permissions, and current source value. The Stacksync issues dashboard supports retry and revert; retry reads current source values, so verify the intended record state before acting.

What does a BigQuery and Demandbase integration cost?

Use the current Stacksync pricing page and confirm the supported implementation with the team. Scope the required objects, record volume, update frequency, initial load, and support needs when comparing a managed connector with native or custom development.

Which records should the BigQuery and Demandbase pilot start with?

Start by reviewing Proposed contact table in BigQuery (choose its name) in BigQuery and People (Persons) in Demandbase. Check how these records relate in your workflow, then confirm the actual fields and supported operations. Test record matching and one failed or repeated update before adding more records.

Data warehouse / Marketing · Two-way sync platform

BigQuery and Demandbase integration

Plan how BigQuery and Demandbase should share data across your business. Work with Stacksync engineers on record mapping, system access, and the requirements for running the integration.

Scope your workflow with an integration engineer
Review the systems, records, and updates you need

Integration planning

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BigQuery

Demandbase

Review the integration scope

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Proposed workflow

Contact reporting workflow

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting eventA change involving the proposed contact table in BigQuery or Demandbase People (Persons) needs a defined result in the other system.

Start with the proposed contact table in BigQuery and Demandbase People (Persons). Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve the organization relationship and any owner or consent references required by the destination.
Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.

What to verifyTest an email change, two records sharing an email, and a person associated with multiple organizations.

Review records and field ownership

Proposed record relationships

Records to connect

Use these examples to define record matching and field ownership for your technical review.

Download the mapping worksheet

CSV · No email required

Example record relationships between BigQuery and Demandbase
BigQuery record	Demandbase record	Record matching	Field ownership
Proposed contact tableProposed table; choose its name and schema.Reporting dataset	People (Persons)Proposed record; confirm Stacksync object support.	Use a stable person/contact ID and an explicit cross-system lookup. Email can change and can be shared, so treat it as a matching clue rather than a universal key.	Keep consent and communication preferences under an agreed authority; a general contact update must not silently resubscribe someone.
Proposed campaign or audience tableProposed table; choose its name and schema.Reporting dataset	CampaignsProposed record; confirm Stacksync object support.	Keep campaign ID, channel/account context, and separate audience membership IDs.	Marketing owns campaign activation and audience eligibility; analytical metrics do not imply permission to launch a campaign.
Proposed company tableProposed table; choose its name and schema.Reporting dataset	AccountsProposed record; confirm Stacksync object support.	Retain the source company ID and the destination customer/company ID. Separate legal entities, subsidiaries, and business units; a shared name or web domain is insufficient.	Assign ownership separately for relationship details and finance-controlled billing details.

These relationships do not establish connector availability. Review the required connection and record operations with Stacksync.

Record coverage to review

Use documented coverage where available. Catalog record types are starting points for review and do not confirm Stacksync support.

BigQuery

Read and write support varies by record

Record types covered in the setup guide

Record types	Coverage and requirements
Tables	See connector requirements. Confirm field permissions and sync direction.

Read the BigQuery connector guide

Demandbase

Connection and object support require review

Record types to review with Stacksync

Record types	Coverage and requirements
Accounts Opportunities People (Persons) Activities Campaigns Account & Person Lists (Audiences)	Confirm support for this record type and the direction you need.

Discuss Demandbase requirements

Connection essentials

Confirm Stacksync support and account requirements for undocumented connections. Interface information alone does not establish connector availability.

View setup requirements and limits

Connection requirement	BigQuery	Demandbase
Integration interface	GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs	REST APIs (Demandbase One) - a B2B API (company/contact search, details, and match with async bulk enrichment jobs), a read-only Data Export API and a write-capable Data Import API (both async bulk jobs), and a Subscription API for webhook change alerts.
Authentication	Dedicated Google Cloud service account and JSON key	Confirm the credentials, API plan, and permissions required for Demandbase.
Change detection	The setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project.	Confirm how Stacksync detects changes for this connector and the objects you need.
Read access	Available for supported records	Confirm with Stacksync
Write access	Available for supported records	Confirm with Stacksync

Enterprise controls

Security and control for your integrations

Explore security controls

Compliance and data transfers

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.

SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
DPF US-EU-UK-CH

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:

Record-level recovery

Inspect sync errors and use retry and revert controls to resolve failed updates.

Read the recovery guide

Implementation

Technical reference

Review setup, record relationships, testing, and recovery for your implementation.

Authentication, permissions and API limits

Connection requirements and limits

BigQuery

Integration interface: GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs
Authentication: Dedicated Google Cloud service account and JSON key; enable the APIs and grant the roles in the authorization guide.
Change detection: The setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project.
Read access: Available for supported records
Write access: Available for supported records

Setup requirements

Enable BigQuery, Cloud Run, Cloud Resource Manager, and Eventarc APIs in the target project.
Create a dedicated service account with the roles in the authorization guide and supply its credentials through Stacksync.

Limitations to check

Only tables are supported in the saved guide; ordinary and materialized views are excluded.
Google Cloud quotas and the selected write method affect capacity; validate the current project limits before a large backfill.

Technical documentation

Documentation reviewed 2026-09-15. Check the linked guides for current account and record requirements.

BigQuery setup guide

Demandbase

Integration interface: REST APIs (Demandbase One) - a B2B API (company/contact search, details, and match with async bulk enrichment jobs), a read-only Data Export API and a write-capable Data Import API (both async bulk jobs), and a Subscription API for webhook change alerts.
Authentication: Confirm the credentials, API plan, and permissions required for Demandbase.
Change detection: Confirm how Stacksync detects changes for this connector and the objects you need.
Read access: Confirm with Stacksync
Write access: Confirm with Stacksync

Setup requirements

Identify the Demandbase account, edition, environment, and business objects the integration must access.
Confirm a Stacksync connector or implementation path for Demandbase, including read/write support, authentication, and initial-load limits.

Limitations to check

Confirm Stacksync support for Demandbase and the record types your workflow needs.
Review write-back, deletion handling, update timing, and account limits with the integration team.

Prepare BigQuery and Demandbase access

Set up both accounts before testing the mapping. Use test records where available, and identify the account administrator who can approve access and help resolve setup errors.

BigQuery setup checklist

Enable BigQuery, Cloud Run, Cloud Resource Manager, and Eventarc APIs in the target project.
Create a dedicated service account with the roles in the authorization guide and supply its credentials through Stacksync.

Setup guides: Authorize BigQuery

Demandbase setup checklist

Identify the Demandbase account, edition, environment, and business objects the integration must access.
Confirm a Stacksync connector or implementation path for Demandbase, including read/write support, authentication, and initial-load limits.

Prepare to go live

Record the fields each system can update, the first-load cutoff, both record IDs, the expected update delay, and who handles errors. Complete the tests before production before expanding to more records.

Use the BigQuery and Demandbase planning worksheet to capture these decisions. Record the access owner in the worksheet and enter credentials only in the connection setup.

Talk to an engineer · Review current pricing

Record identity and field ownership

Use these data-model references to describe the records your connection needs. They are planning examples; connector availability and supported operations must be established before implementation.

Download the mapping worksheet · CSV, no email required

Reporting dataset

Proposed contact table in BigQuery (choose its name) / People (Persons)

Plan a contact dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

BigQuery: Your database schema
Demandbase: Object support to establish
Record identity: Use a stable person/contact ID and an explicit cross-system lookup. Email can change and can be shared, so treat it as a matching clue rather than a universal key.
Field ownership: Keep consent and communication preferences under an agreed authority; a general contact update must not silently resubscribe someone.

Fields to include

Source person ID
Display name
Email address
Organization reference
Consent state

Record dependencies: Resolve the organization relationship and any owner or consent references required by the destination.
Validation: Test an email change, two records sharing an email, and a person associated with multiple organizations.
Recovery: Hold ambiguous matches for review and reconcile the person ID before retrying; preserve the consent decision already recorded by its owner.

Reporting dataset

Proposed campaign or audience table in BigQuery (choose its name) / Campaigns

Plan a campaign or audience dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

BigQuery: Your database schema
Demandbase: Object support to establish
Record identity: Keep campaign ID, channel/account context, and separate audience membership IDs.
Field ownership: Marketing owns campaign activation and audience eligibility; analytical metrics do not imply permission to launch a campaign.

Fields to include

Source campaign ID
Channel reference
Status
Audience references
Measurement window

Record dependencies: Resolve contact/audience identities and channel-account references.
Validation: Test a removed audience member, a paused campaign, and one campaign reported across multiple channels.
Recovery: Rebuild the current eligible audience before retrying activation or membership updates.

Reporting dataset

Proposed company table in BigQuery (choose its name) / Accounts

Plan a company dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

BigQuery: Your database schema
Demandbase: Object support to establish
Record identity: Retain the source company ID and the destination customer/company ID. Separate legal entities, subsidiaries, and business units; a shared name or web domain is insufficient.
Field ownership: Assign ownership separately for relationship details and finance-controlled billing details.

Fields to include

Source record ID
Legal or display name
Business-unit reference
Lifecycle status

Record dependencies: Resolve parent organizations, business units, and currency references before dependent transactions.
Validation: Use two organizations with similar names and one with multiple business units. Verify that an update reaches the intended entity only.
Recovery: Repair the cross-system ID relationship before retrying dependent records; do not merge companies solely to remove a sync error.

Reporting dataset

Proposed deal or opportunity table in BigQuery (choose its name) / Opportunities

Plan a deal or opportunity dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

BigQuery: Your database schema
Demandbase: Object support to establish
Record identity: Keep the opportunity/deal ID separate from any later order or invoice ID.
Field ownership: The sales process owns qualification and stage changes; downstream financial records have their own state and approval rules.

Fields to include

Source deal ID
Stage
Amount and currency
Expected close date
Company reference

Record dependencies: Map the customer and sales pipeline before the opportunity; map stage values deliberately.
Validation: Test a reopened won deal, a stage with no destination equivalent, and an amount using a different currency.
Recovery: Suspend downstream creation for a rejected deal and review whether an order already exists before retrying.

Reporting dataset

Proposed event or activity table in BigQuery (choose its name) / Activities

Plan a event or activity dataset while preserving its source meaning.

Planning example. Stacksync support for the required connection and record operations needs a technical review.

BigQuery: Your database schema
Demandbase: Object support to establish
Record identity: Keep the source event ID, source system, occurrence time, and ingestion time. Use an explicit duplicate-detection key.
Field ownership: Decide whether the destination stores an immutable history or only a current-state summary.

Fields to include

Source event ID
Event type
Occurred-at time
Related record ID
Payload version

Record dependencies: Resolve the related customer, user, or transaction identity without assuming the event ID is the entity ID.
Validation: Deliver the same event twice, then an older event after a newer one; verify duplicate and ordering behavior.
Recovery: Identify side effects already completed before replaying an event; use the agreed deduplication key.

Compare integration approaches

Choose a method around one example record and the update your business needs. Use Proposed contact table in BigQuery (choose its name) / People (Persons) to review record matching and confirm Stacksync support for the required operations. Compare ongoing sync, a custom workflow, and a scheduled export against that requirement.

Stacksync managed sync

Best fit: Review compatibility with a Stacksync engineer using an example of the records and updates you need.
Operating responsibility: Fits ongoing record synchronization when the required operations are supported. Add workflow steps for approvals or business actions that go beyond copying fields.
Before you choose: Check record matching: Use a stable person/contact ID and an explicit cross-system lookup. Email can change and can be shared, so treat it as a matching clue rather than a universal key. Verify field coverage, deletion handling, and how changes are detected.

Native vendor integration

Best fit: A vendor-built integration may fit if it supports your BigQuery and Demandbase record types.
Operating responsibility: Can reduce setup for a supported workflow. You may need another method for records or business steps it does not cover.
Before you choose: First check whether either vendor offers this integration. If available, verify Proposed contact table in BigQuery (choose its name) / People (Persons), update direction, account tier, and related-record handling.

Custom API or workflow

Best fit: Consider when BigQuery and Demandbase need a transformation, approval, or action outside a direct record sync.
Operating responsibility: Provides control over business steps; the team owns credentials, version changes, error queues, and reconciliation.
Before you choose: Verify endpoint permissions, pagination, quotas, duplicate detection, and failure recovery.

File or scheduled snapshot

Best fit: Consider for a one-time BigQuery / Demandbase migration or a reporting need with an explicit freshness window.
Operating responsibility: Can simplify a bounded transfer; later changes and deletion history require another extraction or a separately designed incremental process.
Before you choose: Record the extraction cutoff, source IDs, encoding, date/number formats, and reconciliation totals.

Workflow scenarios and expected results

Contact reporting workflow

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting event: A change to the selected Proposed contact table in BigQuery (choose its name) or People (Persons) record needs a defined result in the other system.

Start with BigQuery Proposed contact table in BigQuery (choose its name) and Demandbase People (Persons). Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve the organization relationship and any owner or consent references required by the destination.
Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.

Expected result: Test an email change, two records sharing an email, and a person associated with multiple organizations.

If it fails: Hold ambiguous matches for review and reconcile the person ID before retrying; preserve the consent decision already recorded by its owner.

Campaign or audience reporting workflow

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting event: A change to the selected Proposed campaign or audience table in BigQuery (choose its name) or Campaigns record needs a defined result in the other system.

Start with BigQuery Proposed campaign or audience table in BigQuery (choose its name) and Demandbase Campaigns. Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve contact/audience identities and channel-account references.
Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.

Expected result: Test a removed audience member, a paused campaign, and one campaign reported across multiple channels.

If it fails: Rebuild the current eligible audience before retrying activation or membership updates.

Company reporting workflow

Planning example. Stacksync support for the required connection and record operations needs a technical review.

Starting event: A change to the selected Proposed company table in BigQuery (choose its name) or Accounts record needs a defined result in the other system.

Start with BigQuery Proposed company table in BigQuery (choose its name) and Demandbase Accounts. Use the record-matching and field-ownership rules from your mapping worksheet.
Resolve parent organizations, business units, and currency references before dependent transactions.
Test a normal update and one failed or repeated update in the supported direction. Keep both record IDs with the test results.

Expected result: Use two organizations with similar names and one with multiple business units. Verify that an update reaches the intended entity only.

If it fails: Repair the cross-system ID relationship before retrying dependent records; do not merge companies solely to remove a sync error.

Define an explicit handoff between BigQuery and Demandbase

This is an evaluation scenario; connector and operation support require confirmation.

Starting event: A business event involving a selected business dataset needs a defined response involving Accounts or Opportunities.

Document what the source event means and which destination record or action should respond. A similar name does not establish a shared entity.
Retain separate IDs and choose whether the destination is a report, a new work item, or a change to an existing record.
Assign an approval owner and a duplicate-detection rule before running an action. Use a custom workflow only after its endpoint support is confirmed.

Expected result: An example input has an unambiguous destination and expected result; repeated delivery produces only the intended change.

If it fails: Resolve missing identity or ambiguous business meaning before retrying; route unsupported operations to the implementation owner.

Initial load and acceptance testing

Keep both record IDs with the expected and actual result. Reconcile the same filters and time window in each system.

Proposed contact table in BigQuery (choose its name) / People (Persons)

Test case

Test an email change, two records sharing an email, and a person associated with multiple organizations.

Expected result

The expected contact relationship is preserved with no duplicate action or unintended write.

Proposed campaign or audience table in BigQuery (choose its name) / Campaigns

Test case

Test a removed audience member, a paused campaign, and one campaign reported across multiple channels.

Expected result

The expected campaign or audience relationship is preserved with no duplicate action or unintended write.

Proposed company table in BigQuery (choose its name) / Accounts

Test case

Use two organizations with similar names and one with multiple business units. Verify that an update reaches the intended entity only.

Expected result

The expected company relationship is preserved with no duplicate action or unintended write.

Proposed deal or opportunity table in BigQuery (choose its name) / Opportunities

Test case

Test a reopened won deal, a stage with no destination equivalent, and an amount using a different currency.

Expected result

The expected deal or opportunity relationship is preserved with no duplicate action or unintended write.

Direction and permissions

Test case

Bring an example source record and the intended destination operation to the compatibility review. Confirm the supported route before granting write access.

Expected result

Only an approved, supported direction and permitted fields are written.

Freshness and reconciliation

Test case

Measure source and destination times for the selected records under normal load and a burst. Reconcile IDs and values using the same filters and cutoff.

Expected result

The process meets its agreed freshness target and reconciliation has no unexplained differences.

Failed updates, retries and recovery

Start with the failed record and the destination error, then inspect the source value, field requirements, and access.

Rejected or repeated contact change

Investigate

Inspect BigQuery Proposed contact table in BigQuery (choose its name) and Demandbase People (Persons), their IDs, and the destination error.

Next action

Hold ambiguous matches for review and reconcile the person ID before retrying; preserve the consent decision already recorded by its owner.

Rejected or repeated campaign or audience change

Investigate

Inspect BigQuery Proposed campaign or audience table in BigQuery (choose its name) and Demandbase Campaigns, their IDs, and the destination error.

Next action

Rebuild the current eligible audience before retrying activation or membership updates.

Rejected or repeated company change

Investigate

Inspect BigQuery Proposed company table in BigQuery (choose its name) and Demandbase Accounts, their IDs, and the destination error.

Next action

Repair the cross-system ID relationship before retrying dependent records; do not merge companies solely to remove a sync error.

A record type or update is unavailable

Investigate

Check the BigQuery and Demandbase connector guides, account permissions, and any operations marked On Request.

Next action

Ask the integration team to confirm a supported way to handle that record. Verify whether it needs connector configuration or a separate workflow step.

Source and destination disagree after a retry

Investigate

Compare current source values, destination validation, identity mappings, and any side effects already completed.

Next action

Stacksync issue retry reads the current source state. Decide the intended state before retrying or reverting; reconcile downstream effects separately.

Read the Stacksync issues dashboard guide for retry and revert behavior.

Change detection and update delivery

How updates move between BigQuery and Demandbase

See how each system detects changes and which updates the other system can receive. Each direction has its own permissions and record requirements.

BigQuery Demandbase Direction requires confirmation

Detect changesThe setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project.

Apply updatesConfirm that Stacksync can create or update the records you need in Demandbase.

Demandbase BigQuery Direction requires confirmation

Detect changesConfirm how Stacksync detects changes for this connector and the objects you need.

Apply updatesConfirm that Stacksync can create or update the records you need in BigQuery.

Update timing and record limits

Measure initial-load and ongoing-change latency separately. Source detection, selected objects, account limits, and destination validation determine the observed delay.
Google Cloud quotas and the selected write method affect capacity; validate the current project limits before a large backfill.
Review write-back, deletion handling, update timing, and account limits with the integration team.

FAQ