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Data warehouse ⇄ Developer tools

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

Keep BigQuery and PagerDuty 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 PagerDuty

Close the gap between analytics and operations: BigQuery holds the record while PagerDuty runs the day-to-day work, and Stacksync keeps the two in step in real time, in both directions.

BigQuery is the central store where teams keep Projects, Tables, Partitioned tables, Clustered tables for reporting and analysis; PagerDuty runs the operational side of engineering work — tracking issues, moving messages and events, watching systems, and managing users and access. The two overlap wherever the same operational data matters to both: the Services, Users, Teams, Schedules produced in PagerDuty are exactly what analysts want to measure in BigQuery, and the curated rows in BigQuery are what should drive the next action in PagerDuty. When that overlap is bridged by nightly ETL or hand-written scripts, dashboards lag a day behind reality and the tools that should react to warehouse signals never see them.

Stacksync syncs Projects, Tables, Partitioned tables, Clustered tables in BigQuery with Services, Users, Teams, Schedules in PagerDuty field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync matches records on a stable external key, keeps every copy consistent, and resolves conflicts by rules you set — so analytics and operations work from the same current data instead of two drifting copies.

Common use cases

  • 01 Feed ML feature tables in BigQuery from operational systems on a continuous schedule
  • 02 Land CRM and ERP records in BigQuery continuously so dashboards reflect business systems without nightly batch jobs
  • 03 Write Schedules and Escalation Policies from a workforce tool or source-of-truth spreadsheet so rotations and overrides stay consistent across teams.
  • 04 Mirror Services and their integration keys with a CMDB or service catalog so ownership and tier metadata stay aligned in both directions.

Common sync patterns

One shared record, kept consistent

Where both systems track the same entity, a change on either side propagates to the other, ending the manual reconciliation between the operational copy and the warehouse copy.

Keep user and access records aligned

Where PagerDuty manages users, directory, or access data, those records stay current in BigQuery — and can be provisioned back from it — so ownership and permissions match across both.

Operational data lands in BigQuery for analytics

Records created in PagerDuty — issues, events, messages, metrics, or user changes — replicate into BigQuery tables as they happen, so reporting runs on current data instead of last night's export.

What you can sync between BigQuery and PagerDuty

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 PagerDuty objects How this pairing syncs
Clustered tables Supported; clustering is transparent to the sync. Escalation Policies Ordered rules routing incidents to users and schedules; read and written two-way to codify paging logic from a source of truth. Clustered tables is specific to BigQuery and Escalation Policies to PagerDuty — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. On-Calls Computed view of who is on call now, derived from schedules and escalation policies; read-only, ideal for pushing current responders into other systems. Datasets is specific to BigQuery and On-Calls to PagerDuty — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Notes and Log Entries Notes are writable to append context to an incident; log entries are a read-only record of every action taken on that incident. Projects is specific to BigQuery and Notes and Log Entries to PagerDuty — 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. Incidents Core records with status of triggered, acknowledged, or resolved plus urgency and assignments; created, updated, and resolved two-way, with V3 webhooks firing on each transition. Tables is specific to BigQuery and Incidents to PagerDuty — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Services Technical services that group incidents and hold integration keys; read and written two-way, with service.created, service.updated, and service.deleted webhook events. Partitioned tables is specific to BigQuery and Services to PagerDuty — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and PagerDuty

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

PagerDuty BigQuery Sub-second propagation

DetectionPagerDuty notifies Stacksync of record changes through webhook events. V3 webhook subscriptions push incident.* and service.* events (triggered, acknowledged, escalated, resolved, created, updated).

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.
  • PagerDuty: REST API allows 960 requests per minute per token and returns HTTP 429 when exceeded; the Events API is rate-limited separately per integration key.
What ships with BigQuery ⇄ PagerDuty

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or PagerDuty 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 PagerDuty record.

Observability

Monitoring

Track your BigQuery ⇄ PagerDuty 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 PagerDuty.

How the BigQuery and PagerDuty 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

PagerDuty

Integration surface
REST API v2 (plus Events API v2 for inbound alerts)
Authentication
REST API token via the Authorization: Token header (account-level for full access or user-level scoped to the user's permissions), or OAuth 2.0 (Authorization Code / PKCE); the Events API v2 uses a per-service routing (integration) key
Change detection
V3 webhook subscriptions push incident.* and service.* events (triggered, acknowledged, escalated, resolved, created, updated); list endpoints also support polling with updated_at and since/until windows
Capabilities
read · write · webhooks
Rate limits
REST API allows 960 requests per minute per token and returns HTTP 429 when exceeded; the Events API is rate-limited separately per integration key.
PagerDuty setup guide
How it works

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

    Choose tables

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

BigQuery and PagerDuty 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
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 421 integrations available for BigQuery and PagerDuty.

Popular · 8 of 421
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