Real-time
Real-time sync
Changes in Aviato or BigQuery instantly reflect in both systems. No stale data, no manual imports.
Business productivity ⇄ Data warehouse
Aviato to BigQuery integration — real-time data sync
Keep Aviato and BigQuery in sync without custom scripts. Cut weeks of integration work, eliminate silent data drift, and give your team a single, reliable source of truth.
- SOC 2 and 6 other compliance frameworks
- POC with real engineers in minutes
Adopted by fast-scaling companies moving mission-critical data in real time
Case study
Migrated from MuleSoft
Case study
Migrated from Celigo
Migrated from Heroku Connect
Migrated from Matillion
Case study
Migrated from Fivetran
Case study
Migrated from Celigo
Why teams connect Aviato and BigQuery
Get the data locked inside Aviato into BigQuery as live tables, and send results back where Aviato can use them, without writing a pipeline.
Aviato is a read-only source: Stacksync reads its data in real time and delivers it into BigQuery, so BigQuery always reflects the current state of Aviato — without exports, scripts, or schedulers.
Whatever Aviato is used for, it accumulates data the rest of the company wants to analyze, and that data usually sits behind an API rather than in the warehouse. Building and babysitting an extraction pipeline is the tax most teams pay for it.
Common use cases
- 01 Keep a warehouse table of tracked private companies refreshed on a schedule for downstream scoring and reporting
- 02 Pull founder and team data into a sourcing database to flag new startups that match an investment thesis
- 03 Feed ML feature tables in BigQuery from operational systems on a continuous schedule
- 04 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs
Common sync patterns
History that outlives the tool
A continuously synced copy in BigQuery preserves a queryable record even as data ages out of Aviato or gets changed inside it.
Analytics on Aviato's data
Records and events from Aviato land in BigQuery as queryable tables, current within seconds and ready to join with the rest of the warehouse.
Cross-tool reporting
Combine Aviato's data with data from every other synced system to answer questions no single tool can.
What you can sync between Aviato and BigQuery
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.
| Aviato objects | BigQuery objects | How this pairing syncs | |
|---|---|---|---|
| Person Founder and employee profiles linked to current and past companies | Tables The syncable unit: only tables can be synced per the Stacksync docs. | Person is specific to Aviato and Tables to BigQuery — each maps to any object or custom field on the other side. | |
| Funding Round Round-level records with stage, amount, date, and participating investors | Partitioned tables Synced like regular tables; partition columns map to target fields. | Funding Round is specific to Aviato and Partitioned tables to BigQuery — each maps to any object or custom field on the other side. | |
| Investor Funds and angels connected to the rounds and companies they back | Clustered tables Supported; clustering is transparent to the sync. | Investor is specific to Aviato and Clustered tables to BigQuery — each maps to any object or custom field on the other side. | |
| Headcount Snapshot Point-in-time employee counts used to track company growth over time | Datasets Organizational container — you pick which dataset’s tables to sync. | Headcount Snapshot is specific to Aviato and Datasets to BigQuery — each maps to any object or custom field on the other side. | |
| Employment Record Person-to-company links with role and tenure that model team movement | Projects Connection scope: the service account grants access per project. | Employment Record is specific to Aviato and Projects to BigQuery — each maps to any object or custom field on the other side. |
How changes propagate between Aviato and BigQuery
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.
Aviato BigQuery Interval-based propagation
DetectionStacksync polls Aviato for changes on an incremental schedule, reading only records changed since the previous pass. Polling-based: re-query tracked records on a schedule and diff against the last synced state.
DeliveryEach detected change is applied to BigQuery as a row-level write, with types converted between the two schemas.
BigQuery Aviato 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").
DeliveryAviato does not accept inbound record writes, so this direction carries requests rather than records: Aviato's output flows back as field updates on the originating BigQuery records.
Rate-limit considerations
- Aviato: Request volume is credit- and rate-limited per plan; schedule refreshes in batches rather than per-record calls.
- 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.
What ships with Aviato ⇄ BigQuery
Connect Aviato and BigQuery for flexible, real-time data sync.
Real-time sync, workflow automation, event queues, EDI, and monitoring, for every Aviato–BigQuery connection.
No-code + pro-code
Workflow automation
Trigger automated workflows whenever Aviato or BigQuery 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 Aviato or BigQuery record.
Observability
Monitoring
Track your Aviato ⇄ BigQuery sync health, view errors, and replay failed events in one click.
Trading partners
EDI
Transform legacy EDI complexity into simple database interactions between Aviato and BigQuery.
How the Aviato and BigQuery connectors work
Aviato
- Integration surface
- REST API returning JSON, with search/filter endpoints for querying company and people records
- Authentication
- API key passed on each request
- Change detection
- Polling-based: re-query tracked records on a schedule and diff against the last synced state; no native change feed is assumed
- Capabilities
- read
- Rate limits
- Request volume is credit- and rate-limited per plan; schedule refreshes in batches rather than per-record calls
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
How it works
How to connect Aviato to BigQuery — 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.
- 01
Connect your apps
Authenticate Aviato and BigQuery with each platform's native method — OAuth, API keys, or service accounts — plus secure options like SSH tunneling, IP whitelisting, and VPC peering.
Aviato
connected
BigQuery
connected
OAuth 2.0SSH tunnelSSL certificateVPC peering - 02
Choose tables
Pick the Aviato and BigQuery 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.
objects · Aviato ⇄ BigQueryCustomers 12,480Sales Orders 8,213Invoices 5,902Items 1,344 - 03
Map fields
Fields map automatically even when names and types differ. Stacksync handles transformation and type casting for you, zero configuration required.
Aviato BigQueryCompany company_name textEmail email textAmount amount numericCreated created_at timestamp
FAQ
Aviato and BigQuery integration FAQ
-
Yes. Stacksync provides a managed, real-time integration between Aviato and BigQuery — Aviato is a read-only source, so data flows from it into the other system: authenticate both systems, choose the objects to sync, map fields visually, and changes propagate in milliseconds — no code required.
-
Stacksync pricing is usage-based and starts at $1,000/month, including the managed Aviato and BigQuery connectors, real-time two-way sync, monitoring, and support. That replaces building and maintaining a custom Aviato–BigQuery integration in-house.
-
Yes — Stacksync ships production-grade connectors for both Aviato and BigQuery. The connectors handle authentication, schema detection, rate limits, and retries; you configure the sync, and Stacksync operates it.
-
Change detection on Aviato: Polling-based: re-query tracked records on a schedule and diff against the last synced state; no native change feed is assumed. On BigQuery: 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. Each detected change propagates to the other side in milliseconds, with field-level conflict resolution and an inspectable event log.
-
On the Aviato side: Acquisition / Exit Event, Company, Person, Funding Round, plus custom fields where Aviato exposes them. On the BigQuery side: Clustered tables, Datasets, Projects, Tables. Stacksync auto-detects both schemas and converts types between the two systems.
-
Aviato is a read-only source, so this integration runs one-way: Stacksync reads from Aviato in real time and delivers into BigQuery. Field mapping and monitoring work the same as for two-way pairs.
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.
SOC 2 Type II
ISO 27001
HIPAA BAA
GDPR
CCPA
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:
Related integrations
Every pair below is a real-time, two-way sync. Search all 322 integrations available for Aviato and BigQuery.
Popular · 5 of 322
-
Aviato ⇄ Rockset -
BigQuery ⇄ Lemlist -
BigQuery ⇄ Lusha -
BigQuery ⇄ Microsoft 365 -
BigQuery ⇄ Microsoft Dynamics 365
No integrations match .
