Can you integrate BigQuery with HubSpot?

Stacksync connects BigQuery and HubSpot with two-way sync for supported records. Authorize the accounts, select the record types, and map compatible fields. Both connectors support reading and writing; object permissions determine the available fields. Use the Issues dashboard to inspect and resolve rejected updates.

Can BigQuery and HubSpot sync in both directions?

Stacksync supports two-way sync between writable records in both systems. The chosen objects and fields must permit updates in both directions. Read-only fields can supply values but cannot receive updates. Stacksync can read database views; it does not write back to the view.

What should be ready before the BigQuery and HubSpot 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. Have a HubSpot Super Admin authorize Stacksync through the account connection flow. Use the pair worksheet to record ownership and acceptance criteria.

How fast does the BigQuery to HubSpot 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 HubSpot?

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 HubSpot 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 HubSpot pilot start with?

Start by reviewing Proposed company table in BigQuery (choose its name) in BigQuery and Company in HubSpot. 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 / CRM · Two-way sync platform

BigQuery and HubSpot integration — two-way sync

Keep supported BigQuery and HubSpot records aligned with two-way sync. Give each team access to current data while controlling which system can update each field.

Field mappings and sync direction under your control
Inspect and resolve record errors in one dashboard

Two-way sync for supported objects

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Example workflow

Company reporting workflow

Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.

Starting eventA change involving the proposed company table in BigQuery or HubSpot Company needs a defined result in the other system.

Start with the proposed company table in BigQuery and HubSpot Company. 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.

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

Review records and field ownership

Mapping essentials

Records and field ownership

Match records by stable IDs and assign an owner for each field. The record notes identify the coverage to check.

Download the mapping worksheet

CSV · No email required

Example record relationships between BigQuery and HubSpot
BigQuery record	HubSpot record	Record matching	Field ownership
Proposed company tableProposed table; choose its name and schema.Reporting dataset	CompanyDocumented record: Supported	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.
Proposed contact tableProposed table; choose its name and schema.Reporting dataset	ContactDocumented record: Supported	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 deal or opportunity tableProposed table; choose its name and schema.Reporting dataset	DealDocumented record: Supported	Keep the opportunity/deal ID separate from any later order or invoice ID.	The sales process owns qualification and stage changes; downstream financial records have their own state and approval rules.

Use writable fields from the connected accounts. Read values from read-only fields without writing changes back to them, and define deletion handling separately.

Why teams connect BigQuery and HubSpot

Put your BigQuery and HubSpot data to work

Start with the work your team needs to complete: keep records up to date, make operational data available for reporting, or move data to a new system. Choose one BigQuery and HubSpot workflow to test, define what success looks like, and decide who handles failed updates.

Build board-level pipeline dashboards on BigQuery instead of HubSpot's native reports.
Enrich HubSpot Contacts with attributes calculated from warehouse Tables, kept current by two-way sync.
Analyze Ticket volume against Deal size in BigQuery to price support into renewals.

What records can you sync?

Start with the records your workflow needs. Check each system’s read and write requirements before mapping fields.

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

HubSpot

Read and write support varies by record

Record types covered in the setup guide

Record types	Coverage and requirements
Contact Company Deal Line Item Product Ticket Quote Goal Owner Pipeline Stages Audit Subscriptions HubDB Forms Calls Communications Emails Meetings Notes Post Mail Tasks Marketing Emails Invoices Attachments Custom objects (all) Associations Users Services Listings Courses Appointments Commerce Payments Email Events Property History	✅ Supported. Confirm field permissions and sync direction.
Campaigns	🕘 Coming soon. Confirm field permissions and sync direction.
Feedback Submissions	❌ Not supported. Confirm field permissions and sync direction.

Read the HubSpot connector guide

Connection essentials

Review how each system connects, detects changes, and permits access to your records.

View setup requirements and limits

Connection requirement	BigQuery	HubSpot
Integration interface	GoogleSQL via the BigQuery REST API, client libraries, JDBC/ODBC drivers, and the Storage Read/Write APIs	REST API (CRM v3)
Authentication	Dedicated Google Cloud service account and JSON key	OAuth
Change detection	The setup provisions notification services using Eventarc and Cloud Run in the customer Google Cloud project.	Record updates and association changes need separate validation.
Read access	Available for supported records	Available for supported records
Write access	Available for supported records	Available for supported records

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

HubSpot

Integration interface: REST API (CRM v3)
Authentication: OAuth (choose HubSpot account and authorize Stacksync); requires a HubSpot 'Super Admin' to grant access; optional "Grant access to sensitive fields" checkbox for sensitive/highly sensitive fields
Change detection: Record updates and association changes need separate validation. Association detection uses full scans unless Associations CDC Boost is configured for an eligible account.
Read access: Available for supported records
Write access: Available for supported records

Setup requirements

Have a HubSpot Super Admin authorize Stacksync through the account connection flow.
Include association tables when relationships between records are needed. Configure Associations CDC Boost separately when eligible.

Limitations to check

Custom objects require an eligible HubSpot plan. Custom id properties that collide with native record IDs are excluded from sync.
Associations CDC Boost requires setup and is unavailable on Free and Starter accounts; association timing can differ from ordinary records.

Technical documentation

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

HubSpot setup guide

Prepare BigQuery and HubSpot 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

HubSpot setup checklist

Have a HubSpot Super Admin authorize Stacksync through the account connection flow.
Include association tables when relationships between records are needed. Configure Associations CDC Boost separately when eligible.

Setup guides: Authorize HubSpot

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

See Stacksync in action · Review current pricing

Record identity and field ownership

Keep each record tied to its source ID. Use the references below to choose field owners and preserve relationships between records.

Download the mapping worksheet · CSV, no email required

Reporting dataset

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

Plan a company dataset while preserving its source meaning.

Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.

BigQuery: Your database schema
HubSpot: Documented record · ✅ Supported
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.

References: HubSpot: Company documentation

Reporting dataset

Proposed contact table in BigQuery (choose its name) / Contact

Plan a contact dataset while preserving its source meaning.

Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.

BigQuery: Your database schema
HubSpot: Documented record · ✅ Supported
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.

References: HubSpot: Contact documentation

Reporting dataset

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

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

Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.

BigQuery: Your database schema
HubSpot: Documented record · ✅ Supported
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.

References: HubSpot: Deal documentation

Reporting dataset

Proposed support case or ticket table in BigQuery (choose its name) / Ticket

Plan a support case or ticket dataset while preserving its source meaning.

Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.

BigQuery: Your database schema
HubSpot: Documented record · ✅ Supported
Record identity: Retain the ticket ID and distinguish parent tickets, follow-ups, and merged cases.
Field ownership: Agree which queue controls status and assignee; keep private comments distinct from public replies.

Fields to include

Source ticket ID
Requester reference
Status
Priority
Assigned team

Record dependencies: Resolve requester, organization, team, and status references before ticket updates.
Validation: Test a merged ticket, a private note, a reopened case, and an attachment with restricted access.
Recovery: Check whether a reply or notification was already sent before replaying ticket actions.

References: HubSpot: Ticket documentation

Reporting dataset

Proposed product or catalog item table in BigQuery (choose its name) / Product

Plan a product or catalog item dataset while preserving its source meaning.

Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.

BigQuery: Your database schema
HubSpot: Documented record · ✅ Supported
Record identity: Distinguish the product ID, variant ID, SKU, and price-list entry; they are not interchangeable keys.
Field ownership: Assign ownership for catalog content, price, and stock separately.

Fields to include

Source product ID
SKU or variant reference
Description
Unit of measure
Price-list reference

Record dependencies: Resolve units, variants, categories, and applicable price lists before order lines.
Validation: Test two variants of one product, a changed SKU, and a price that applies to only one market or currency.
Recovery: Repair the variant or price-list reference before retrying affected order lines; preserve existing transaction prices.

References: HubSpot: Product documentation

Reporting dataset

Proposed customer invoice table in BigQuery (choose its name) / Invoices

Plan a customer invoice dataset while preserving its source meaning.

Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.

BigQuery: Your database schema
HubSpot: Documented record · ✅ Supported
Record identity: Retain the invoice ID, issuer/legal entity, and original order reference; invoice numbers alone may overlap.
Field ownership: The financial system owns posting and accounting treatment. A posted invoice may need a credit or adjustment process instead of an overwrite.

Fields to include

Source invoice ID
Customer or supplier reference
Line totals
Currency
Posting/payment status

Record dependencies: Resolve the customer or supplier, account codes, tax, currency, and accounting period before posting.
Validation: Test a posted invoice, a partial payment, tax rounding, and a closed accounting period. Compare financial totals within the same entity and currency.
Recovery: Determine whether a transaction posted before retrying. Follow the approved adjustment process for posted records.

References: HubSpot: Invoices documentation

Compare integration approaches

Use BigQuery Proposed company table in BigQuery (choose its name) and HubSpot Company for the first pilot. Verify its identity and permitted direction, then add dependent records only when the acceptance checks pass.

Stacksync managed sync

Best fit: Two-way sync for supported objects. Choose the records, writable fields, and permissions for your workflow.
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: 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. 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 HubSpot 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 company table in BigQuery (choose its name) / Company, update direction, account tier, and related-record handling.

Custom API or workflow

Best fit: Consider when BigQuery and HubSpot 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 / HubSpot 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

Company reporting workflow

Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.

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

Start with BigQuery Proposed company table in BigQuery (choose its name) and HubSpot Company. 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.

Contact reporting workflow

Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.

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

Start with BigQuery Proposed contact table in BigQuery (choose its name) and HubSpot Contact. 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.

Deal or opportunity reporting workflow

Object-specific availability and field permissions determine this mapping. Use the restrictions shown for each record type.

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

Start with BigQuery Proposed deal or opportunity table in BigQuery (choose its name) and HubSpot Deal. Use the record-matching and field-ownership rules from your mapping worksheet.
Map the customer and sales pipeline before the opportunity; map stage values deliberately.
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 reopened won deal, a stage with no destination equivalent, and an amount using a different currency.

If it fails: Suspend downstream creation for a rejected deal and review whether an order already exists before retrying.

Define an explicit handoff between BigQuery and HubSpot

Validate the selected objects and operations even where connector-level direction is documented.

Starting event: A business event involving a selected business dataset needs a defined response involving Contact or Company.

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 company table in BigQuery (choose its name) / Company

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 contact table in BigQuery (choose its name) / Contact

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

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.

Proposed support case or ticket table in BigQuery (choose its name) / Ticket

Test case

Test a merged ticket, a private note, a reopened case, and an attachment with restricted access.

Expected result

The expected support case or ticket relationship is preserved with no duplicate action or unintended write.

Direction and permissions

Test case

Check that each connected account can read and write the chosen objects. Exercise both directions with a test record before enabling production changes.

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 company change

Investigate

Inspect BigQuery Proposed company table in BigQuery (choose its name) and HubSpot Company, 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.

Rejected or repeated contact change

Investigate

Inspect BigQuery Proposed contact table in BigQuery (choose its name) and HubSpot Contact, 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 deal or opportunity change

Investigate

Inspect BigQuery Proposed deal or opportunity table in BigQuery (choose its name) and HubSpot Deal, their IDs, and the destination error.

Next action

Suspend downstream creation for a rejected deal and review whether an order already exists before retrying.

A record type or update is unavailable

Investigate

Check the BigQuery and HubSpot 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 HubSpot

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

BigQuery HubSpot Timing depends on the connected systems

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

Apply updatesSelected writable fields update in HubSpot. Its permissions and validation rules still apply; rejected records can be inspected in the Issues dashboard.

HubSpot BigQuery Timing depends on the connected systems

Detect changesRecord updates and association changes need separate validation. Association detection uses full scans unless Associations CDC Boost is configured for an eligible account.

Apply updatesSelected writable fields update in BigQuery. Its permissions and validation rules still apply; rejected records can be inspected in the Issues dashboard.

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.
Associations CDC Boost requires setup and is unavailable on Free and Starter accounts; association timing can differ from ordinary records.

FAQ

BigQuery and HubSpot integration FAQ

Guides and resources

Next step

See your workflow in Stacksync

Walk through your BigQuery and HubSpot records, field mappings, and requirements with an integration engineer.

See Stacksync in action Explore plans and pricing

BigQuery and HubSpot integration — two-way sync

Built for teams where self-serve, reliability and scale matter

BigQuery

HubSpot

Compliance and data transfers

SSO & SCIM

Alerts

Secure connection options

Record-level recovery

Authentication, permissions and API limits

Connection requirements and limits

BigQuery

Setup requirements

Limitations to check

Technical documentation

HubSpot

Setup requirements

Limitations to check

Technical documentation

BigQuery setup checklist

HubSpot setup checklist

Prepare to go live

Record identity and field ownership

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

Proposed contact table in BigQuery (choose its name) / Contact

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

Proposed support case or ticket table in BigQuery (choose its name) / Ticket

Proposed product or catalog item table in BigQuery (choose its name) / Product

Proposed customer invoice table in BigQuery (choose its name) / Invoices

Compare integration approaches

Stacksync managed sync

Native vendor integration

Custom API or workflow

File or scheduled snapshot

Workflow scenarios and expected results

Company reporting workflow

Contact reporting workflow

Deal or opportunity reporting workflow

Define an explicit handoff between BigQuery and HubSpot

Initial load and acceptance testing

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

Proposed contact table in BigQuery (choose its name) / Contact

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

Proposed support case or ticket table in BigQuery (choose its name) / Ticket

Direction and permissions

Freshness and reconciliation

Failed updates, retries and recovery

Rejected or repeated company change

Rejected or repeated contact change

Rejected or repeated deal or opportunity change

A record type or update is unavailable

Source and destination disagree after a retry

Change detection and update delivery

How updates move between BigQuery and HubSpot

Update timing and record limits

More connections to explore