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Load Snipe-IT data to DuckDB

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

SourceSnipe-ITSnipe-IT API DocumentationDestinationDuckDBIn-process analytical database. The default local destination for dlt pipelines.

Snipe-IT is an IT asset management software that provides a JSON REST API for performing operations programmatically. Everything needed to build a working Snipe-IT → 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 Snipe-IT 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 Snipe-IT 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 Snipe-IT 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.


Snipe-IT API at a glance

Base URLhttps://your-snipe-it-instance.com/api/v1
Example endpointGET api/v1/hardware
Records found atrows
Authenticationall requests require a Bearer token in the Authorization header plus Accept and Content-Type headers — sent in the Authorization header, prefixed Bearer
Also requiredAccept, Content-Type
PaginationOffset-based page size via limit. Uses offset-based pagination. 'limit' defaults to 50; 'offset' defaults to 0. The maximum number of results per request is controlled by the 'MAX_RESULTS' server-side configuration (default is often 500 or 250 depending on version). No cursor tokens are used.
Incremental fieldoffset
API referencehttps://snipe-it.readme.io/reference/authenticating-with-the-api

These values come from the Snipe-IT API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the Snipe-IT API?

Authentication is performed using a personal access token sent in the Authorization header with the Bearer scheme. Additionally, requests must include 'Accept: application/json' and 'Content-Type: application/json' headers to function correctly.

1. Get your credentials

To obtain API credentials, log in to your Snipe-IT instance and navigate to your user profile by clicking your avatar in the top right corner. Select 'Manage API Keys' (or the 'API Keys' tab), click 'Create New Token', provide a descriptive name for the token, and copy the generated key immediately, as it will not be displayed again.

2. Add them to .dlt/secrets.toml

[sources.snipe_it_source] api_key = "your_snipe_it_api_token_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 Snipe-IT data can I load into DuckDB?

These are the Snipe-IT endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
hardware/api/v1/hardwareGETrowsList all assets
users/api/v1/usersGETrowsList all users
accessories/api/v1/accessoriesGETrowsList all accessories
components/api/v1/componentsGETrowsList all components
categories/api/v1/categoriesGETrowsList all categories
models/api/v1/modelsGETrowsList all models

How do I load only new Snipe-IT records?

Snipe-IT exposes offset on api/v1/hardware, so dlt can request only the records that changed since the last run. Set it as the cursor_path and dlt tracks the high-water mark for you between runs.

{"name": "hardware", "endpoint": { "path": "api/v1/hardware", "data_selector": "rows", "incremental": {"cursor_path": "offset", "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 Snipe-IT pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /api/v1/hardware and /api/v1/users from the Snipe-IT API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def snipe_it_source(api_token=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://your-snipe-it-instance.com/api/v1", "auth": {"type": "bearer", "token": api_token}, }, "resources": [ {"name": "hardware", "endpoint": {"path": "api/v1/hardware", "data_selector": "rows"}}, {"name": "users", "endpoint": {"path": "api/v1/users", "data_selector": "rows"}} ], } yield from rest_api_resources(config) def load_snipe_it_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="snipe_it_pipeline", destination="duckdb", dataset_name="snipe_it_data", ) load_info = pipeline.run(snipe_it_source()) print(load_info) if __name__ == "__main__": load_snipe_it_to_duckdb()

Run it with python snipe_it_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 Snipe-IT 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("snipe_it_pipeline").dataset() df = data.hardware.df() print(df.head())

SQL:

SELECT * FROM snipe_it_data.hardware LIMIT 10;

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


How do I deploy the Snipe-IT 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 Snipe-IT 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 Snipe-IT 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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