Data ingestion

Data source

A data source in Feast refers to raw underlying data that users own (e.g. in a table in BigQuery). Feast does not manage any of the raw underlying data but instead, is in charge of loading this data and performing different operations on the data to retrieve or serve features.

Feast uses a time-series data model to represent data. This data model is used to interpret feature data in data sources in order to build training datasets or materialize features into an online store.

Below is an example data source with a single entity column (driver) and two feature columns (trips_today, and rating).

Feast supports primarily time-stamped tabular data as data sources. There are many kinds of possible data sources:

  • Batch data sources: ideally, these live in data warehouses (BigQuery, Snowflake, Redshift), but can be in data lakes (S3, GCS, etc). Feast supports ingesting and querying data across both.

  • Stream data sources: Feast does not have native streaming integrations. It does however facilitate making streaming features available in different environments. There are two kinds of sources:

    • Push sources allow users to push features into Feast, and make it available for training / batch scoring ("offline"), for realtime feature serving ("online") or both.

    • [Alpha] Stream sources allow users to register metadata from Kafka or Kinesis sources. The onus is on the user to ingest from these sources, though Feast provides some limited helper methods to ingest directly from Kafka / Kinesis topics.

  • (Experimental) Request data sources: This is data that is only available at request time (e.g. from a user action that needs an immediate model prediction response). This is primarily relevant as an input into on-demand feature views, which allow light-weight feature engineering and combining features across sources.

Batch data ingestion

Ingesting from batch sources is only necessary to power real-time models. This is done through materialization. Under the hood, Feast manages an offline store (to scalably generate training data from batch sources) and an online store (to provide low-latency access to features for real-time models).

A key command to use in Feast is the materialize_incremental command, which fetches the latest values for all entities in the batch source and ingests these values into the online store.

Materialization can be called programmatically or through the CLI:

Code example: programmatic scheduled materialization

This snippet creates a feature store object which points to the registry (which knows of all defined features) and the online store (DynamoDB in this case), and

# Define Python callable
def materialize():
  repo_config = RepoConfig(
    registry=RegistryConfig(path="s3://[YOUR BUCKET]/registry.pb"),
  store = FeatureStore(config=repo_config)

# (In production) Use Airflow PythonOperator
materialize_python = PythonOperator(
Code example: CLI based materialization

How to run this in the CLI

CURRENT_TIME=$(date -u +"%Y-%m-%dT%H:%M:%S")
feast materialize-incremental $CURRENT_TIME

How to run this on Airflow

# Use BashOperator
materialize_bash = BashOperator(
    bash_command=f'feast materialize-incremental {}',

Batch data schema inference

If the schema parameter is not specified when defining a data source, Feast attempts to infer the schema of the data source during feast apply. The way it does this depends on the implementation of the offline store. For the offline stores that ship with Feast out of the box this inference is performed by inspecting the schema of the table in the cloud data warehouse, or if a query is provided to the source, by running the query with a LIMIT clause and inspecting the result.

Stream data ingestion

Ingesting from stream sources happens either via a Push API or via a contrib processor that leverages an existing Spark context.