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Business productivity ⇄ Data warehouse

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

Keep Atlassian 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.

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  • POC with real engineers in minutes

Adopted by fast-scaling companies moving mission-critical data in real time

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Why teams connect Atlassian and BigQuery

Get the data locked inside Atlassian into BigQuery as live tables, and send results back where Atlassian can use them, without writing a pipeline.

Data teams sync Atlassian to BigQuery to analyze engineering delivery at warehouse scale. Jira Issues, Boards and Sprints, and Custom Fields land in BigQuery Datasets as Tables, where they join marketing, revenue, and product data already in the warehouse. Partitioned tables keep large Issue histories fast to query.

Stacksync syncs Confluence Pages, Confluence Spaces, Jira Issues, Jira Projects from Atlassian into tables in BigQuery continuously, handling schema, rate limits, and retries. Because the sync is bi-directional, results computed in BigQuery can also be written back into fields in Atlassian where the tool can use them.

Common use cases

  • 01 Join Jira Projects with revenue data in the same BigQuery Project to measure engineering cost per initiative.
  • 02 Retain full Issue history in Partitioned tables beyond what Jira reporting exposes.
  • 03 Standardize delivery KPIs across teams from one warehouse Dataset.
  • 04 Create Jira issues from records in other systems, such as onboarding tasks generated from a closed-won CRM deal.

Common sync patterns

Delivery analytics pipeline

Jira Issues and Custom Fields stream into BigQuery Tables partitioned by date for cycle-time and throughput analysis.

Sprint health datasets

Boards and Sprints sync into a BigQuery Dataset feeding BI dashboards on sprint completion and carryover.

Comment-level history

Issue Comments load into Clustered tables for efficient per-project drill-down.

What you can sync between Atlassian 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.

Atlassian objects BigQuery objects How this pairing syncs
Jira Projects Containers that scope issues, workflows, and permissions for a sync. Tables The syncable unit: only tables can be synced per the Stacksync docs. Jira Projects is specific to Atlassian and Tables to BigQuery — each maps to any object or custom field on the other side.
Boards and Sprints Agile structures read to report on sprint contents and status. Partitioned tables Synced like regular tables; partition columns map to target fields. Boards and Sprints is specific to Atlassian and Partitioned tables to BigQuery — each maps to any object or custom field on the other side.
Issue Comments Threaded discussion synced into linked tickets in external systems. Clustered tables Supported; clustering is transparent to the sync. Issue Comments is specific to Atlassian and Clustered tables to BigQuery — each maps to any object or custom field on the other side.
Attachments Files on issues mirrored to paired records where needed. Datasets Organizational container — you pick which dataset’s tables to sync. Attachments is specific to Atlassian and Datasets to BigQuery — each maps to any object or custom field on the other side.
Custom Fields Instance-specific fields (customfield IDs) that carry most business-specific data in syncs. Projects Connection scope: the service account grants access per project. Custom Fields is specific to Atlassian and Projects to BigQuery — each maps to any object or custom field on the other side.

How changes propagate between Atlassian 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.

Atlassian BigQuery Sub-second propagation

DetectionAtlassian notifies Stacksync of record changes through webhook events. Webhooks on issue and page events, plus JQL polling on the updated timestamp for backfill.

DeliveryEach detected change is applied to BigQuery as a row-level write, with types converted between the two schemas.

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

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.
What ships with Atlassian ⇄ BigQuery

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Atlassian 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 Atlassian or BigQuery record.

Observability

Monitoring

Track your Atlassian ⇄ BigQuery sync health, view errors, and replay failed events in one click.

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Atlassian and BigQuery.

How the Atlassian and BigQuery connectors work

Atlassian

Integration surface
REST APIs per product (Jira Cloud and Confluence Cloud)
Authentication
OAuth 2.0 (3LO) for apps or API tokens with basic auth for scripts
Change detection
Webhooks on issue and page events, plus JQL polling on the updated timestamp for backfill
Capabilities
read · write · webhooks

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
How it works

How to connect Atlassian 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.

  1. 01

    Connect your apps

    Authenticate Atlassian 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.

    • OAuth 2.0
    • SSH tunnel
    • VPC peering
    Atlassian connected
    BigQuery connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

    Pick the Atlassian 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.

    • Standard objects
    • Custom objects
    • Auto-schema
    objects · Atlassian ⇄ BigQuery
    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
    Atlassian BigQuery
    Company company_name text
    Email email text
    Amount amount numeric
    Created created_at timestamp
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FAQ

Atlassian and BigQuery 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 480 integrations available for Atlassian and BigQuery.

Popular · 7 of 480
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