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Data warehouse ⇄ Storage

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

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

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Why teams connect Databricks and Sharepoint

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

Databricks 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 Databricks, or a result in Databricks 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 Change Data Feed, Catalogs, Schemas, Delta Tables in Databricks with List items, Drive items (document libraries), Columns and content types, Permissions and sharing 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 Serve ML feature outputs computed in Databricks to production apps through a synced operational store.
  • 02 Land CRM and ERP records in Delta tables continuously so lakehouse models work from current operational data.
  • 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 Databricks, 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 Change Data Feed, Catalogs, Schemas, Delta Tables in Databricks, so file metadata can be joined against the rest of your data and reported on.

Where Databricks computes the labels: push them onto the files

Classifications, scores, or status derived in Databricks are written back onto the matching List items, Drive items (document libraries), Columns and content types, Permissions and sharing in Sharepoint as metadata or tags, so the file store reflects what analytics decided.

What you can sync between Databricks 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.

Databricks objects Sharepoint objects How this pairing syncs
Volumes Unity Catalog file storage used for staging bulk loads. Permissions and sharing Role assignments and sharing links on sites, list items, and drive items; read and written to manage access, requiring Sites.Manage.All or Sites.FullControl.All scopes. Volumes is specific to Databricks and Permissions and sharing to Sharepoint — each maps to any object or custom field on the other side.
SQL Warehouses The compute endpoint a sync connects to for query execution. Site pages Modern site pages via /sites/{id}/pages; read page content, layout, and metadata for indexing and content governance. SQL Warehouses is specific to Databricks and Site pages to Sharepoint — each maps to any object or custom field on the other side.
Change Data Feed Row-level change records on Delta tables that drive incremental reads. 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. Change Data Feed is specific to Databricks and Sites to Sharepoint — each maps to any object or custom field on the other side.
Catalogs Top level of the Unity Catalog namespace, scoping which schemas a sync can address. 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. Catalogs is specific to Databricks and Lists to Sharepoint — each maps to any object or custom field on the other side.
Schemas Group tables and views; syncs typically target a dedicated schema per source system. 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. Schemas is specific to Databricks and List items to Sharepoint — each maps to any object or custom field on the other side.
Delta Tables The primary read and write target; operational data lands here as managed or external tables. 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. Delta Tables is specific to Databricks and Drive items (document libraries) to Sharepoint — each maps to any object or custom field on the other side.

How changes propagate between Databricks 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.

Databricks Sharepoint Sub-second propagation

DetectionChanges in Databricks are captured at the source via change data capture — no polling loop against its API. Delta Lake Change Data Feed for row-level changes.

DeliveryEach detected change is written to Sharepoint through its API, with automatic retries and rate-limit backoff.

Sharepoint Databricks 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 Databricks as a row-level write, with types converted between the two schemas.

Rate-limit considerations

  • Databricks: Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits.
  • 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 Databricks ⇄ Sharepoint

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

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

Real-time

Two-way sync

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

No-code + pro-code

Workflow automation

Trigger automated workflows whenever Databricks 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 Databricks or Sharepoint record.

Observability

Monitoring

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

Trading partners

EDI

Transform legacy EDI complexity into simple database interactions between Databricks and Sharepoint.

How the Databricks and Sharepoint connectors work

Databricks

Integration surface
SQL over JDBC/ODBC via SQL warehouses, plus a REST API including statement execution
Authentication
Personal access tokens or OAuth machine-to-machine credentials for service principals
Change detection
Delta Lake Change Data Feed for row-level changes; otherwise incremental polling on watermark columns
Capabilities
read · write · CDC
Rate limits
Throughput depends on the SQL warehouse size; API calls are subject to workspace rate limits

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 Databricks 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 Databricks 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
    Databricks connected
    Sharepoint connected
    OAuth 2.0
    SSH tunnel
    SSL certificate
    VPC peering
  2. 02

    Choose tables

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

Databricks 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 541 integrations available for Databricks and Sharepoint.

Popular · 7 of 541
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