Description

BigQuery data sources allow for the retrieval of historical feature values from BigQuery for building training datasets as well as materializing features into an online store.

• Either a table reference or a SQL query can be provided.

• No performance guarantees can be provided over SQL query-based sources. Please use table references where possible.

Examples

Using a table reference

from feast import BigQuerySource

my_bigquery_source = BigQuerySource(
    table_ref="gcp_project:bq_dataset.bq_table",
)

Using a query

from feast import BigQuerySource

BigQuerySource(
    query="SELECT timestamp as ts, created, f1, f2 "
          "FROM `my_project.my_dataset.my_features`",
)

Configuration options are available here.

Description

Redshift data sources allow for the retrieval of historical feature values from Redshift for building training datasets as well as materializing features into an online store.

• Either a table name or a SQL query can be provided.

• No performance guarantees can be provided over SQL query-based sources. Please use table references where possible.

Examples

Using a table name

from feast import RedshiftSource

my_redshift_source = RedshiftSource(
    table="redshift_table",
)

Using a query

from feast import RedshiftSource

my_redshift_source = RedshiftSource(
    query="SELECT timestamp as ts, created, f1, f2 "
          "FROM redshift_table",
)

Configuration options are available here.

Description

File data sources allow for the retrieval of historical feature values from files on disk for building training datasets, as well as for materializing features into an online store.

Example

from feast import FileSource
from feast.data_format import ParquetFormat

parquet_file_source = FileSource(
    file_format=ParquetFormat(),
    file_url="file:///feast/customer.parquet",
)

Configuration options are available here.

Please see Data Source for an explanation of data sources.