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Data warehouse ⇄ Storage

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

Keep BigQuery and Sharepoint 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 Sharepoint

Bridge query-ready tables and stored files: BigQuery and Sharepoint keep the same records in step, in real time, in both directions.

BigQuery keeps the tables and query results a business reports on; Sharepoint keeps the raw files, documents, and objects that the same business produces and shares. The two overlap wherever a dataset lives as both — a file dropped in Sharepoint that has to become rows in BigQuery, or a result in BigQuery that people downstream need back as a file in Sharepoint. When that overlap is bridged by manual export and import or an overnight job, one side spends the day working from a stale copy.

Stacksync syncs Projects, Tables, Partitioned tables, Clustered tables in BigQuery with Site pages, Sites, Lists, List items in Sharepoint field by field, in real time, and in both directions. You decide which system owns which fields; Stacksync keeps every copy consistent and resolves conflicts by rules you set.

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 Stream list and library changes through delta query and change-notification webhooks into a warehouse for audit, access-review, and reporting.
  • 04 Two-way sync SharePoint List items with a database table so internal apps read and write structured records in SQL without calling the SharePoint API directly.

Common sync patterns

One dataset, kept consistent both ways

Where the same dataset lives as a file in Sharepoint and a table in BigQuery, a change on either side propagates to the other, ending the drift between the file people read and the table people query.

Where Sharepoint holds the file inventory: make it queryable

The catalog of documents, owners, and folders in Sharepoint appears as Projects, Tables, Partitioned tables, Clustered tables in BigQuery, so file metadata can be joined against the rest of your data and reported on.

Where BigQuery computes the labels: push them onto the files

Classifications, scores, or status derived in BigQuery are written back onto the matching Site pages, Sites, Lists, List items in Sharepoint as metadata or tags, so the file store reflects what analytics decided.

What you can sync between BigQuery and Sharepoint

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 Sharepoint objects How this pairing syncs
Datasets Organizational container — you pick which dataset’s tables to sync. Sites SharePoint site collections and subsites via /sites; read site properties and discover the document libraries (drives) and lists each site contains, addressed by hostname and site path or site ID. Datasets is specific to BigQuery and Sites to Sharepoint — each maps to any object or custom field on the other side.
Projects Connection scope: the service account grants access per project. Lists Custom lists and libraries via /sites/{id}/lists; create and read lists along with their columns and content types so a target system can mirror or provision list definitions. Projects is specific to BigQuery and Lists to Sharepoint — 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. List items Rows in a SharePoint list via /lists/{id}/items with a fields facet of column values; full create, read, update, and delete, synced two-way with database rows. Tables is specific to BigQuery and List items to Sharepoint — each maps to any object or custom field on the other side.
Partitioned tables Synced like regular tables; partition columns map to target fields. Drive items (document libraries) Files and folders in a library's drive via /drives and /drive/root; upload, download, move, delete, and read per-item metadata, synced two-way with a store. Partitioned tables is specific to BigQuery and Drive items (document libraries) to Sharepoint — each maps to any object or custom field on the other side.
Clustered tables Supported; clustering is transparent to the sync. Columns and content types Field definitions and content types on a site or list; read the schema and create columns so database fields map cleanly onto SharePoint list fields. Clustered tables is specific to BigQuery and Columns and content types to Sharepoint — each maps to any object or custom field on the other side.

How changes propagate between BigQuery and Sharepoint

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

Sharepoint BigQuery Sub-second propagation

DetectionSharepoint notifies Stacksync of record changes through webhook events. Delta query (pull) tracks created, updated, and deleted list items (/lists/{id}/items/delta) and drive items (/drives/{id}/root/delta) since the last.

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.
  • Sharepoint: SharePoint Online throttles per app, returning HTTP 429 (or 503) with a Retry-After header in seconds and IETF RateLimit-Limit, RateLimit-Remaining, and RateLimit-Reset headers; throttled requests still count toward limits, so clients honor the greater of Retry-After and RateLimit-Reset with exponential backoff.
What ships with BigQuery ⇄ Sharepoint

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever BigQuery or Sharepoint 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 Sharepoint record.

Observability

Monitoring

Track your BigQuery ⇄ Sharepoint 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 Sharepoint.

How the BigQuery and Sharepoint 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

Sharepoint

Integration surface
Microsoft Graph REST API (graph.microsoft.com/v1.0 and beta) covering SharePoint sites, lists, list items, columns, content types, and document-library drives and drive items; plus the classic SharePoint REST API (_api/web) and CSOM. Supports Graph JSON $batch (up to 20 requests per call).
Authentication
OAuth 2.0 via the Microsoft identity platform (Microsoft Entra ID). An app registration holds delegated or application (app-only) scopes such as Sites.Read.All, Sites.ReadWrite.All, Sites.Selected, Sites.Manage.All, or Sites.FullControl.All, plus Files.ReadWrite.All for drive content; application permissions require tenant admin consent.
Change detection
Delta query (pull) tracks created, updated, and deleted list items (/lists/{id}/items/delta) and drive items (/drives/{id}/root/delta) since the last deltaLink; change-notification subscriptions (push webhooks) POST near-real-time notifications for list and drive/root changes to a notification URL and must be renewed before they expire. Classic SharePoint list webhooks are also available.
Capabilities
read · write · webhooks
Rate limits
SharePoint Online throttles per app, returning HTTP 429 (or 503) with a Retry-After header in seconds and IETF RateLimit-Limit, RateLimit-Remaining, and RateLimit-Reset headers; throttled requests still count toward limits, so clients honor the greater of Retry-After and RateLimit-Reset with exponential backoff.
How it works

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

    Choose tables

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

BigQuery and Sharepoint 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.

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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 533 integrations available for BigQuery and Sharepoint.

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