Chaining features

Features are reusable. This means it is possible to create robust data pipelines within Lynk - by adding features to an entity, based on features of other (related) entities.

Common challenges in data modeling are questions like "how should I model this?" and "was this done before, somewhere else?"

Lynk takes care of such challenges by providing a structured way to model data (defining features on entities via YAML files), governing it with the Governance layer and making it all accessible via the Studio interface.

Virtual data assets

Virtual data assets are data assets that live "virtually" within Lynk. Meaning, there is no materialized asset on the underlying data source. A virtual data assets is created for each entity within Lynk, where the entity is the "asset" and it's features are the "fields".

Creating features from related entities

For example, let's assume we have an entity called user and another entity called team, and their relation is many-to-one. You can create a feature called is_active_user on the user level and consume it for creating a metric feature active_users_count on the team entity level.

# user.yml (virtual data asset)

asset: user
...

measures:
- name: active_users_count
  description: count of active users
  sql: SUM(IFF(is_active_user = true, 1, 0))

# team.yml (team entity)
...
features: 

- type: metric
  name: active_users_count
  asset: user
  field: active_users_count
  filters: null

Note that the asset in this case is the related entity name (see related entities).

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