featurebyte.SourceTable.create_observation_table

create_observation_table(

name: str,

sample_rows: Optional[int]=None,

columns: Optional[list[str]]=None,

columns_rename_mapping: Optional[dict[str, str]]=None,

context_name: Optional[str]=None

) -> ObservationTable

Description

Creates an ObservationTable from the SourceTable. When you specify the columns and the columns_rename_mapping parameters, make sure that the table has:

• column(s) containing entity values with an accepted serving name.
• a column containing historical points-in-time in UTC. The column name must be "POINT_IN_TIME".

Parameters

• name: str
  Observation table name.
  
  
• sample_rows: Optional[int]
  Optionally sample the source table to this number of rows before creating the observation table.
  
  
• columns: Optional[list[str]]
  Include only these columns when creating the observation table. If None, all columns are included.
  
  
• columns_rename_mapping: Optional[dict[str, str]]
  Rename columns in the source table using this mapping from old column names to new column names when creating the observation table. If None, no columns are renamed.
  
  
• context_name: Optional[str]
  Context name for the observation table.
  
  

Returns

• ObservationTable
  
  

Examples

>>> ds = fb.FeatureStore.get(<feature_store_name>).get_data_source()
>>> source_table = ds.get_source_table(
...   database_name="<data_base_name>",
...   schema_name="<schema_name>",
...   table_name=<table_name>
... )

>>> observation_table = source_table.create_observation_table(
...   name="<observation_table_name>",
...   sample_rows=desired_sample_size,
...   columns=[<timestamp_column_name>, <entity_column_name>],
...   columns_rename_mapping={
...     timestamp_column_name: "POINT_IN_TIME",
...     entity_column_name: <entity_serving_name>,
...   },
...   context_id=context_id,
... )