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Data warehouse ⇄ Business productivity

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

Keep BigQuery and GitHub 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

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Why teams connect BigQuery and GitHub

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

Engineering leaders sync GitHub into BigQuery to measure delivery from the source of record. Repositories, Pull Requests, and Workflow runs land in BigQuery Tables, where they can be queried alongside incident, cost, and product data instead of living only in the GitHub API.

Stacksync syncs Pull Requests, Commits, Releases, Workflow runs (Actions) from GitHub 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 GitHub where the tool can use them.

Common use cases

  • 01 Trend Pull Request cycle time per Repository across quarters using clustered BigQuery Tables.
  • 02 Correlate Workflow run failures with Commit volume to find flaky pipelines.
  • 03 Keep a durable record of Releases and closed Issues in BigQuery for audit reviews.
  • 04 Mirror repository, PR, and workflow-run data into a Postgres database for engineering-metrics reporting.

Common sync patterns

Delivery metrics warehouse

GitHub Pull Requests and Commits sync to partitioned BigQuery Tables for cycle-time and review-latency queries.

CI reliability tracking

Workflow runs (Actions) stream into a BigQuery Dataset to track failure rates and duration per Repository.

Release audit trail

GitHub Releases and Issues are captured in BigQuery for compliance and change-management reporting.

What you can sync between BigQuery and GitHub

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 GitHub objects How this pairing syncs
Clustered tables Supported; clustering is transparent to the sync. Releases Tagged versions synced into changelogs, CRMs, or customer-notification systems. Clustered tables is specific to BigQuery and Releases to GitHub — each maps to any object or custom field on the other side.
Datasets Organizational container — you pick which dataset’s tables to sync. Workflow runs (Actions) CI results synced into incident and reporting systems. Datasets is specific to BigQuery and Workflow runs (Actions) to GitHub — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Organizations and Teams Membership data synced with identity systems and HR directories for access reviews. Projects is specific to BigQuery and Organizations and Teams to GitHub — 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. Users Author and assignee identities matched to internal directories. Tables is specific to BigQuery and Users to GitHub — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Labels and Milestones Classification fields mapped to statuses and sprints in external trackers. Partitioned tables is specific to BigQuery and Labels and Milestones to GitHub — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and GitHub

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

GitHub BigQuery Sub-second propagation

DetectionGitHub notifies Stacksync of record changes through webhook events. Webhooks with a broad event catalog covering issues, pull requests, pushes, and releases.

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.
  • GitHub: Authenticated REST requests are limited to 5,000 per hour per user; GitHub Apps scale limits per installation.
What ships with BigQuery ⇄ GitHub

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or GitHub 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 GitHub record.

Observability

Monitoring

Track your BigQuery ⇄ GitHub 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 GitHub.

How the BigQuery and GitHub 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

GitHub

Integration surface
REST API and GraphQL API
Authentication
OAuth 2.0, fine-grained personal access tokens, or GitHub App installation tokens
Change detection
Webhooks with a broad event catalog covering issues, pull requests, pushes, and releases; polling for backfill
Capabilities
read · write · webhooks
Rate limits
Authenticated REST requests are limited to 5,000 per hour per user; GitHub Apps scale limits per installation.
How it works

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

    Choose tables

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

BigQuery and GitHub 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 493 integrations available for BigQuery and GitHub.

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