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
Configuration options are available .
from feast import BigQuerySource
BigQuerySource(
query="SELECT timestamp as ts, created, f1, f2 "
"FROM `my_project.my_dataset.my_features`",
)
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",
)