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Load Channex data to DuckDB

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

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

Channex is a channel manager platform for connecting hotels to various sales channels. Everything needed to build a working Channex → 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 Channex 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 Channex 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 Channex 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.


Channex API at a glance

Base URLhttps://staging.channex.io/api/v1
Example endpointGET bookings
Records found atdata
Authenticationall requests require an API key passed in the 'user-api-key' header — sent in the user-api-key header
PaginationPage-number page size via pagination[limit]. The pagination uses page numbers (indexed from 1) and a limit. Page indexing starts at 1, and the maximum limit per page is 100. The meta field in the response includes 'limit', 'page', and 'total'.
Incremental fieldupdated_at
Record idid
API referencehttps://docs.channex.io/api-v.1-documentation/api-reference

These values come from the Channex API reference — the authoritative source if anything here looks out of date.


How do I authenticate with the Channex API?

All requests require an API key to be passed as the 'user-api-key' header.

1. Get your credentials

Log in to your Channex account and navigate to your User Profile. Locate the API Keys section, click 'Create new API Key', enter a name for the key, and select the desired property permissions. Copy the generated key immediately, as it will only be displayed once. Note: An active subscription is typically required for API access outside of the staging environment.

2. Add them to .dlt/secrets.toml

[sources.channex_source] user_api_key = "your_generated_api_key_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 Channex data can I load into DuckDB?

These are the Channex endpoints dlt can load into DuckDB:

ResourceEndpointMethodData selectorDescription
bookingsbookingsGETdataRetrieve a list of bookings
room_typesroom_typesGETdataRetrieve a list of room types
rate_plansrate_plansGETdataRetrieve a list of rate plans
property_facilitiesproperty_facilitiesGETdataRetrieve a list of property facilities
room_facilitiesroom_facilitiesGETdataRetrieve a list of room facilities

How do I load only new Channex records?

Channex exposes updated_at on bookings, 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": "bookings", "endpoint": { "path": "bookings", "data_selector": "data", "incremental": {"cursor_path": "updated_at", "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 Channex pipeline look like?

A standard dlt REST API pipeline — the same code you would write by hand, loading /properties and /bookings from the Channex API into DuckDB:

import dlt from dlt.sources.rest_api import RESTAPIConfig, rest_api_resources @dlt.source def channex_source(api_key=dlt.secrets.value): config: RESTAPIConfig = { "client": { "base_url": "https://staging.channex.io/api/v1", "auth": {"type": "api_key", "api_key": api_key, "name": "user-api-key", "location": "header"}, }, "resources": [ {"name": "bookings", "endpoint": {"path": "bookings", "data_selector": "data"}}, {"name": "room_types", "endpoint": {"path": "room_types", "data_selector": "data"}} ], } yield from rest_api_resources(config) def load_channex_to_duckdb() -> None: pipeline = dlt.pipeline( pipeline_name="channex_pipeline", destination="duckdb", dataset_name="channex_data", ) load_info = pipeline.run(channex_source()) print(load_info) if __name__ == "__main__": load_channex_to_duckdb()

Run it with python channex_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 Channex 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("channex_pipeline").dataset() df = data.bookings.df() print(df.head())

SQL:

SELECT * FROM channex_data.bookings LIMIT 10;

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


How do I deploy the Channex 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 Channex 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 Channex 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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