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Load Sharefile data to DuckDB

Build a Sharefile to DuckDB pipeline with your coding agent. One prompt scaffolds it with the dltHub AI harness, plus the Sharefile API base URL, auth, endpoints, and incremental loading.

SourceSharefileShareFile API DocumentationDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

ShareFile is a cloud-based content collaboration platform providing a REST API based on the OData specification for managing files, folders, and users. Everything needed to build a working Sharefile → DuckDB pipeline is on this page: the API's base URL, authentication, endpoints, pagination and incremental field — plus a prompt that hands the whole job to your coding agent.


Build your Sharefile to DuckDB pipeline

Paste this prompt into Claude, Codex, or Cursor. The agent does the rest.

Prompt
Run uvx dlthub-init@latest to build a pipeline from Sharefile to DuckDB and run it on dltHub

That scaffolds a dltHub workspace and installs the dltHub AI harness — the project rules, the secrets-management skill, and the dlt MCP server your agent needs to work safely. From there it reads the Sharefile API, proposes the endpoints to load, then writes, runs and validates the pipeline while you review rather than type. Credentials are inspected through MCP tools, so your agent never reads secrets.toml itself. How the LLM-native workflow works →

Prefer to write it yourself? Every fact the agent uses is below.


Sharefile API at a glance

Base URLhttps://{subdomain}.sf-api.com/sf/v3
Example endpointGET Reports
Records found atvalue
Authenticationall requests require an Authorization header with a Bearer token — sent in the Authorization header, prefixed Bearer
PaginationCursor-based page size via $top (default 250). The API uses OData-style pagination parameters $top and $skip. The response typically includes an odata.nextLink containing the URL for the next page. Paging.PageNumber and Paging.PageSize (in POST bodies) are deprecated.
API referencehttps://developer.sharefile.com/gettingstarted/oauth2

These values come from the Sharefile API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the Sharefile API?

Requests to the ShareFile API must include an Authorization header with the format 'Bearer [access_token]'. The access token is obtained via an OAuth2 token endpoint using client credentials or user credentials.

1. Get your credentials

  1. Navigate to the ShareFile API Key Generator page (typically https://api.sharefile.com/apikeys). 2. Log in with your ShareFile account credentials. 3. Provide the required application details, including a 'Redirect URI' (e.g., https://www.getpostman.com/oauth2/callback for testing). 4. Submit the form to generate and retrieve your 'Client ID' and 'Client Secret'.

2. Add them to .dlt/secrets.toml

[sources.sharefile_source] client_id = "your_client_id_here" client_secret = "your_client_secret_here" subdomain = "your_subdomain_here" username = "your_username_here" password = "your_password_here"

dlt reads this file automatically at runtime. With the harness, the setup-secrets skill prompts you for the values and never handles the raw credential in chat. For production, see setting up credentials with dlt.


What Sharefile data can I load into DuckDB?

These are the Sharefile endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
itemsItemsGETvalueReturns a list of items (files/folders)
usersAccounts/AddressBookGETvalueReturns a collection of users in the address book
reportsReportsGETvalueReturns available reports
sessionsSessionsGETReturns current session information
zonesZonesGETvalueReturns list of storage zones

How do I load only new Sharefile records?

The Sharefile API reference does not document a timestamp or sequence field for these endpoints, so there is nothing to advertise here as verified. Pick a field from the endpoints table above that increases with every write, then set it as the cursor_path.

{"name": "reports", "endpoint": { "path": "Reports", # Replace with a field that increases on every write. "incremental": {"cursor_path": "REPLACE_ME", "initial_value": "2024-01-01T00:00:00Z"}, }}

On the first run dlt loads everything from initial_value; on every run after that it requests only what changed and appends with write_disposition="merge" if you set a primary key. See incremental loading.


What does the generated Sharefile pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /oauth/token and /sf/v3/Items from the Sharefile API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def sharefile_source(access_token=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://{subdomain}.sf-api.com/sf/v3", "auth": {"type": "bearer", "token": access_token}, }, "resources": [ {"name": "reports", "endpoint": {"path": "Reports", "data_selector": "value"}}, {"name": "sessions", "endpoint": {"path": "Sessions", "data_selector": "value"}} ], } yield from rest_api_resources(config) def load_sharefile_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="sharefile_pipeline", destination="duckdb", dataset_name="sharefile_data", ) load_info = pipeline.run(sharefile_source()) print(load_info) if __name__ == "__main__": load_sharefile_to_duckdb()

Run it with python sharefile_pipeline.py. The agent iterates on this until it loads cleanly — you review and approve, rather than write it from scratch.


How do I query Sharefile data in DuckDB?

dlt creates one table per resource. Query the loaded data with Python or SQL — or ask your agent to, through the MCP server's execute_sql_query tool.

Python (pandas DataFrame):

import dlt data = dlt.pipeline("sharefile_pipeline").dataset() df = data.sessions.df() print(df.head())

SQL:

SELECT * FROM sharefile_data.sessions LIMIT 10;

See querying your data with dataset and exploring it in marimo notebooks.


How do I deploy the Sharefile to DuckDB pipeline in production?

The pipeline runs locally, which is ideal for prototyping and one-off analysis. When you need it on a schedule, monitored on every load, and shared with your team, deploy the same dlt code on the dltHub platform — no infrastructure to maintain. The prompt above already ends with "run it on dltHub", so your agent can take it there directly.

  • Deploy & schedule — run the pipeline as a managed job with automatic retries.
  • Monitor — observable job queues, alerting, and load metrics for every run.
  • Transform — promote raw Sharefile loads into governed, documented models.
  • Visualize & share — explore data in notebooks and publish live dashboards instead of static screenshots.

Book a demo →


What other destinations can I load Sharefile data to?

dlt loads into any of these — only the destination argument changes:

DestinationExample value
PostgreSQL"postgres"
BigQuery"bigquery"
Snowflake"snowflake"
Redshift"redshift"
Databricks"databricks"
Filesystem (S3, GCS, Azure)"filesystem"

Set dlt.pipeline(destination="snowflake") and add credentials in .dlt/secrets.toml. On the dltHub platform the same pipeline runs against a managed Iceberg lakehouse. See the full destinations list.


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