Tecton now natively supports Array type features with Float32, Float64, Int64, and String type elements. Read More

Tecton is releasing a new Run API to be used for dry-run executing a FeatureView's transformation the same way Tecton will execute it during materialization or at feature retrieval time. Read More

cton is releasing a new API to be used for feature retrieval. For fetching features from the offline store, we've introduced the method get_historical_features. For fetching features from the online store, we've introduced the method get_online_features. Read More

You can now include batch_cluster_config configuration in Feature Table declarations. By specifying an EMRClusterConfig or DatabricksClusterConfig configuration or referencing an ExistingClusterConfig, the ingest materialization jobs will run on a cluster of workers with the specified configuration options like instance type, instance size, and spark configurations. Read More

Existing admin users can now directly grant admin privileges to other users through the Admin console, rather than having to make a request to Tecton support. See the Admin console documentation for more detail. Read More

You can now create additional workspaces with automatic materialization by using the flag --live. Read More

The CLI "API key" commands have been refactored as sub-commands for consistency and clearer help docs. Read More

In addition to monitoring the materialization status for individual Feature Views, we've now added a summary dashboard to easily see if any of the Feature Views in a workspace are stale or have failing materialization jobs. Read More

The Tecton SDK is now being published to PyPI at https://pypi.org/project/tecton/ for easier installation. Read More

Continuous mode for Stream Window Aggregate Feature Views now enables new event data to be included in feature values in less than a second. Read More