feast-teradata 1.0.1

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Feast Teradata Connector

Overview

We recommend you familiarize yourself with the terminology and concepts of feast by reading the official feast documentation.

The feast-teradata library adds support for Teradata as

• OfflineStore
• OnlineStore

Additional, using Teradata as the registry (catalog) is already supported via the registry_type: sql and included in our examples. This means that everything is located in Teradata. However, depending on the requirements, installation, etc, this can be mixed and matched with other systems as appropriate.

Getting Started

To get started, install the feast-teradata library

pip install feast-teradata

Let's create a simple feast setup with Teradata using the standard drivers dataset. Note that you cannot use feast init as this command only works for templates which are part of the core feast library. We intend on getting this library merged into feast core eventually but for now, you will need to use the following cli command for this specific task. All other feast cli commands work as expected.

feast-td init-repo

This will then prompt you for the required information for the Teradata system and upload the example dataset. Let's assume you used the repo name demo when running the above command. You can find the repository files along with a file called test_workflow.py. Running this test_workflow.py will execute a complete workflow for feast with Teradata as the Registry, OfflineStore and OnlineStore.

demo/
    feature_repo/
        driver_repo.py
        feature_store.yml
    test_workflow.py

From within the demo/feature_repo directory, execute the following feast command to apply (import/update) the repo definition into the registry. You will be able to see the registry metadata tables in the teradata database after running this command.

feast apply

To see the registry information in the feast ui, run the following command. Note the --registry_ttl_sec is important as by default it polls every 5 seconds.

feast ui --registry_ttl_sec=120

Example Usage

Now, lets batch read some features for training, using only entities (population) for which we have seen an event for in the last 60 days. The predicates (filter) used can be on anything that is relevant for the entity (population) selection for the given training dataset. The event_timestamp is only for example purposes.

from feast import FeatureStore


store = FeatureStore(repo_path="feature_repo")

training_df = store.get_historical_features(
    entity_df=f"""
            SELECT
                driver_id,
                event_timestamp
            FROM demo_feast_driver_hourly_stats
            WHERE event_timestamp BETWEEN (CURRENT_TIMESTAMP - INTERVAL '60' DAY) AND CURRENT_TIMESTAMP
        """,
    features=[
        "driver_hourly_stats:conv_rate",
        "driver_hourly_stats:acc_rate",
        "driver_hourly_stats:avg_daily_trips"
    ],
).to_df()
print(training_df.head())

The feast-teradata library allows you to use the complete set of feast APIs and functionality. Please refer to the official feast quickstart for more details on the various things you can do. Additionally, if you want to see a complete (but not real-world), end-to-end example workflow example, see the demo/test_workflow.py script. This is used for testing the complete feast functionality.

Release Notes

1.0.1

• Doc: Improve README with better getting started information.
• Fix: Remove pytest from requirements.txt
• Fix: Set minimum python version to 3.8 due to feast dependency on pandas>=1.4.3
• Fix: Updated feast-td types conversion

1.0.0

• Feature: Initial implementation of feast-teradata library