Metadata JSON
Last updated
Last updated
This guide describes the metadata JSON spec.
Create your metadata programmatically. Use the Python API to automatically detect the metadata based on your data.
The metadata for a single table contains the following elements:
(required) "METADATA_SPEC_VERSION": The version of the metadata. If you are using this, the metadata version will be "V1", indicating that it is a multi table dataset that is compatible with SDV version 1.
(required) "tables": A dictionary that maps the table names to the table-specific metadata such as primary keys, column names and data types
(required) "relationships": A list of dictionaries that specify the connections between the tables
The tables dictionary maps each table name to the table-specific metadata. This includes:
(required) "columns": A dictionary that maps the column names to the data types they represent and any other attributes.
"primary_key": The column name that is the primary key in the table
"alternate_keys": A list of column names that can act as alternate keys in the table
When describing a column, you will provide the column name and the data type, known as the sdtype.
The 5 common sdtypes are: "numerical", "datetime", "categorical", "boolean" and "id". Click on the type below to learn more about the type and how to specify it in the metadata.
Boolean columns represent True or False values.
Properties (None)
Annotate groups of columns that represents higher level concepts. Denote the concept using the "relationship_type" keyword, followed by "column_names" with the list of columns involved. The column names can be present in any order.
Each relationship type supports different types of columns. Browse the table below to explore different options.
An address is defined by 2 or more columns that have the following sdtypes: country_code, administrative_unit, state, state_abbr, city, postcode, street_address and secondary_address.
While anyone can add column relationships to their data, SDV Enterprise users will see the highest quality data for the relationships. To learn more about the SDV Enterprise and its extra features, visit our website.
A list of dictionary objects that describe the relationship between 2 connected tables, parent and child. The parent table contains the primary key references while the child table has rows that refer to its parent. Multiple child rows can refer to the same parent row.
"parent_table_name": The name of the parent table
"parent_primary_key": The primary key column in the parent table. This column uniquely identifies each row in the parent table .
"child_table_name": The name of the child table that refers to the parent
"child_foreign_key": The foreign key column in the child table. The values in this column contain a reference to a row in the parent table
Use multiple dictionaries to represent multiple tables.
In some cases, you may have a table that describes multiple, ordered sequences of data such as the one shown below.
You can annotate the sequences within a table by applying the sequence_key and sequence_index keywords.
"sequence_key": A column name of the sequence key, if you have multi-sequence data
The sequence key is a column that identify which row(s) belong to which sequences. This is usually an ID column but it may also be a PII sdtype (such as "phone_number").
This is important for tables that contain multiple sequences. In our example, the sequence key is 'Patient ID' because this column is used to break up the sequences.
If you don't supply a sequence key, the SDV assumes that your table only contains a single sequence. Note: The SDV sequential models do not fully support single sequence data.
"sequence_index": A column name of the sequence index, if you have sequential data
The sequence index determines the spacing between the rows in a sequence. Use this if you have an explicit index such as a timestamp. If you don't supply a sequence index, the SDV assumes there is equal spacing of an unknown unit.
