How to connect SharePoint to Snowflake for analytics and data workflows

Although SharePoint is built for collaboration and content management, it often becomes a quiet home for operational data — project timelines, budget spreadsheets, approval workflows, and more. The problem is that most of this data never leaves SharePoint, making it hard to analyze alongside the rest of your business data. 

Connecting SharePoint to Snowflake changes that. It brings all SharePoint data into a cloud data warehouse where it can be combined with data from other systems and used for reporting, forecasting, and cross-functional analysis.

Learn how to connect SharePoint to Snowflake and how Fivetran simplifies the process.

3 methods to connect SharePoint to Snowflake 

You can move SharePoint data into Snowflake in a few different ways, from building an ETL pipeline from SharePoint to Snowflake yourself or using a managed connector. Here are three common methods.

1. Fivetran’s SharePoint Connector

Fivetran offers two prebuilt connectors for SharePoint data. The SharePoint connector handles files stored in document libraries, including CSV, Excel, and other structured formats. A separate Microsoft Lists connector syncs list data, which is where most of the structured operational content in SharePoint lives.

Setup is simple:

1. Authenticate through Microsoft Entra.
2. Select the SharePoint site and the specific folder or list you want to sync. 

That’s it — Fivetran handles the rest. It offers two sync modes for files:

• Magic Folder mode: Maps each file to its own table.
• Merge mode: Consolidates files matching a naming pattern into a single table and supports incremental syncs.

The system detects changes using the last modified date, so only updated files get re-synced after the initial load.

Best for: Teams that want a working pipeline in minutes without writing code or managing API credentials. Fivetran is ideal when you need both file data and list data flowing into Snowflake on a schedule.

Pros: No custom code, automatic schema handling, and incremental sync support in Merge mode.

Cons: Some niche Excel formatting features not supported, like pivot tables, hyperlinks, and merged cell rows.

2. Snowflake OpenFlow

OpenFlow is Snowflake’s native data integration service. It replaces the now-deprecated Snowflake Connector for SharePoint and connects directly to SharePoint through the Microsoft Graph API, landing files into a Snowflake stage.

To set it up:

1. Register an application in Azure AD and configure the required Microsoft Graph permissions.
2. Generate a PEM certificate and use PowerShell to grant the app access to your SharePoint site.
3. Prepare the Snowflake objects you need, like the database, schema, role, and warehouse.
4. Install and configure the SharePoint connector within OpenFlow.

Best for: Data engineering teams who’re already invested in the Snowflake ecosystem, want a native, Snowflake-managed integration, and are comfortable with Azure AD administration.

Pros: Native to Snowflake and supports unstructured files for AI use cases like RAG and document parsing with Cortex AI.

Cons: Requires Azure AD expertise, PowerShell scripting, and key-pair authentication. Not practical for teams without dedicated engineering resources.

3. Custom API pipeline

Build a custom pipeline using the Microsoft Graph API or the older SharePoint REST API. This approach gives you full control over what data you extract, how you transform it, and when you load it.

A typical custom pipeline involves: 

1. Writing scripts that authenticate with Microsoft Entra.
2. Paginating through SharePoint list items or file metadata. 
3. Handling the response formatting. 
4. Loading everything into Snowflake via the Snowflake Python connector or a staging area in cloud storage.
5. Building your own logic for incremental loads, error handling, and retry behavior.

The biggest operational concern with this approach is API throttling. SharePoint Online limits users to 3,000 requests per five-minute window and applies tenant-level resource unit caps that scale with your license count.

If scripts exceed those limits, Microsoft returns HTTP 429 errors with a Retry-After header. And persistent violations can result in your application being blocked entirely.

Best for: Teams with specific extraction requirements that no pre-built connector supports, or organizations that need to transform data heavily before it reaches Snowflake.

Pros: Full flexibility over data selection, transformation, and scheduling.

Cons: High development and maintenance costs, and operational overhead from owning the authentication logic, throttling management, pagination, schema drift handling, and monitoring.

Benefits of sending SharePoint data to Snowflake

If you’re still stuck on whether or not to connect SharePoint to Snowflake, consider these five benefits:

• Centralized analytics across content and activity: SharePoint data sitting in isolation tells you very little. But once it’s in Snowflake, you can run queries across document metadata and site activity alongside data from other sources like CRM, marketing tools, and finance systems.
• Cross-dataset joins: A SharePoint list to Snowflake pipeline that tracks project progress becomes far more useful when you can join milestone data with billing data to see which projects ran over-budget, or with HR data to understand team allocation patterns.
• Faster reporting without manual exports: Many teams still export SharePoint data to Excel, clean it up manually, and paste it into a report. Automating that pipeline eliminates hours of repetitive work each week and reduces the risk of errors from manual handling.
• Historical archiving and compliance: Snowflake retains historical snapshots of data, allowing you to track how SharePoint lists and documents changed over time. For regulated industries, this creates an audit trail that SharePoint alone doesn’t provide in an easily queryable format.
• A foundation for advanced analytics: Bringing SharePoint data in Snowflake lets data science teams use document metadata, collaboration patterns, and list activity in predictive models. That’s data most organizations have but rarely use because it’s locked inside a collaboration tool. 

Challenges in SharePoint to Snowflake data integration

Depending on the connection method you choose, here are some challenges you might face with the integration:

• SharePoint API rate limits are aggressive. Microsoft enforces strict throttling on SharePoint Online, capping users at 3,000 requests per five minutes and applying tenant-level resource unit quotas. All custom pipelines must implement backoff logic and honor Retry-After headers, or risk having the application blocked.
• Metadata and nested structures add complexity. SharePoint lists contain lookup columns, multi-value fields, and nested metadata that don’t map cleanly to flat table structures. Flattening that data for Snowflake requires transformation logic that needs to be maintained as the SharePoint schema evolves.
• Incremental loading requires careful design. SharePoint doesn’t offer a clean change data capture mechanism. Most approaches rely on the last modified timestamp, which works for files but isn’t always reliable for list-level changes. Getting incremental loads right without duplicating or dropping records takes careful engineering.
• Authentication and permissions are layered. Connecting to SharePoint programmatically involves Microsoft Entra app registration, Graph API permissions, and, in some cases, certificate-based authentication. Each layer adds configuration overhead. Plus, SharePoint permissions can silently break a previously working pipeline without warning.

How Fivetran supports SharePoint to Snowflake integrations

Fivetran removes the engineering overhead from this integration. Instead of building and maintaining custom API pipelines, you get prebuilt connectors that handle authentication, data synchronization, schema changes, and incremental loading automatically.

The SharePoint connector syncs files from document libraries into Snowflake, while the Microsoft Lists connector handles structured list data. Both use Microsoft Entra for authentication and detect changes automatically, so only updated records get re-synced.

Plus, Fivetran handles schema drift without manual intervention — if someone adds a column to a SharePoint list, Fivetran adjusts the destination table accordingly.

Integration with Fivetran also works in the other direction, enabling teams to push enriched data from Snowflake to SharePoint for operational use. 

Fivetran frees your data team to focus on post-ingestion work — whether it’s building models, running analyses, or breaking down the silos that keep operational data locked away from the people who need it. Explore Fivetran’s SharePoint connector today.

FAQ

Is there a connector available for Snowflake and SharePoint?

Yes. Fivetran provides managed connectors for both SharePoint files and Microsoft Lists that load directly into Snowflake. The Fivetran SharePoint connector to Snowflake requires no data engineering effort and no custom code.

How can Fivetran be used with SharePoint?

Fivetran connects to SharePoint through two managed connectors. The SharePoint connector syncs files from document libraries, supporting formats like CSV and Excel. The Microsoft Lists connector syncs structured list data. Both are fully managed and automated, so SharePoint data flows into Snowflake without manual intervention or custom code.

What is the Fivetran SharePoint connector, and how does it work?

The Fivetran SharePoint connector is a fully managed integration that extracts files from SharePoint document libraries and loads them into the destination. It supports two sync modes: Magic Folder mode treats each file as a separate destination table, while Merge mode consolidates files that match a naming pattern into a single table and supports incremental syncs. The connector detects changes using the last modified date, handles nested folder structures, and can replicate unstructured files like documents and images to object storage.

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