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.
Configuration options are available .
from feast import FileSource
from feast.data_format import ParquetFormat
parquet_file_source = FileSource(
file_format=ParquetFormat(),
file_url="file:///feast/customer.parquet",
)FileSource is meant for development purposes only and is not optimized for production use.
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.
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 .
Please see for an explanation of data sources.
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.
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 .