lg-data-pipeline

To build a data pipeline to a Liquid Galaxy rig, you need to parse external API responses into domain models, generate KML features, and transport them via SSH. This skill enforces that strict layering and automates the end-to-end transformation process.

Converting API response data into KML requires parsing the payload into a strict domain model and then generating KML placemarks. This skill manages that transformation while ensuring data contracts and layer rules are followed throughout.

SSH transport for KML data involves securely sending generated visualization payloads to the Liquid Galaxy rig. This skill handles the transport layer, ensuring the KML features generated from your API data are delivered correctly for display.

Yes, you can use this data pipeline skill with real-time API data like USGS earthquake feeds. It fetches the data, converts it into a domain model, generates KML placemarks, and sends it to the LG rig for display.

API to KML transformation pipelines break data contracts when separation of concerns is ignored between API providers, domain models, and KML generation. This skill enforces strict layering to ensure data integrity throughout the flow.

You do not need to manually manage domain models separately when transforming API data. This skill enforces a strict separation of concerns, automatically parsing API responses into domain models before KML generation and SSH transport.