Unified Schema Design#

• The goal is to create a single, unified schema across datasets. This schema aims to strike a balance between flexibility to accommodate arbitrarily shaped data along with consistency in core tables.
• Datasets are built around two concepts: entities and timeseries.
  • Entities are concrete things or objects (a geography, a company, a mortgage application).
  • Timeseries are abstract measures (ie. statistics) related to an entity and a date.
• The core tables are:
  • entities: Contains the entities that are being tracked. For example, Spain, Madrid, etc.
    • Should be something like province_index or weather_station_index to be able to join with the timeseries.
    • This table contains permanent characteristics describing an entity. E.g: for Provinces, the name, the region.
    • Each row represents a distinct entity. The table is wide, in that immutable characteristics are expressed in their own fields.
  • attributes: Attributes are descriptors of a timeseries. An attribute is the equivalent of a characteristic except for the abstract timeseries rather than the concrete entity.
    • Columns:
      • variable_id: Unique identifier for the attribute
      • name: Name of the attribute
      • description: Description of the attribute
      • unit: Unit of the attribute
      • source: Source of the attribute
      • frequency: Frequency of the attribute (daily, monthly, etc.)
      • measurement_type: Type of measurement (e.g. nominal, ordinal, interval, ratio, percentage)
      • Metadata columns;
        • category: Category of the attribute
        • namespace: Namespace of the attribute
        • tags: JSON with tags of the attribute?
        • aggregation_function: Aggregation function to use when aggregating the attribute
  • timeseries: Timeseries are abstract measures (ie. statistics, metrics) related to an entity and a date. Timeseries are temporal statistics or measures centered around an entity and timestamp. For example, GDP of Spain, population of Madrid, etc. Timeseries are abstract concepts (ie. a measure) rather than a concrete thing.
    • Could be something like weather_timeseries to be able to join with the entities.
    • Columns:
      • variable_id: Unique identifier for the attribute
      • geography_id: Unique identifier for the geography
      • date: Date of the metric
      • value: Value of the metric
  • relationships: Contains the relationships between entities. For example, Spain is composed of provinces, Madrid is a province, etc.
    • Relationships can also be temporal – valid for an interval defined by specific start and end dates.
  • characteristics: Descriptors of an entity that are temporal. They have a start date and end date.