Feature View Filtering The Features page now includes a filter dropdown to limit results to specific Feature View types or Families. More filtering options will be coming soon. Beta SDK documentation Tecton’s SDK reference now stable and beta release options,

Tecton SDK v0.2 and beta releases Tecton is introducing a new SDK versioning and release process, starting with the v0.2 release. This new process aims to improve the reliability of our SDK for production applications by creating separate stable and

You can now track the infrastructure costs incurred for individual FeatureViews with AWS Cost Explorer. This functionality can help you identify how to optimize your infrastructure cost with Tecton. Tags configured for a FeatureView are passed through as AWS tags.

Using an on-demand instance for the Spark driver node can make stream processing more reliable by preventing losing the entire cluster to spot termination.

The new Jobs tab in the Web UI makes it easy to keep track of materialization jobs for all of your features. You can navigate to the Jobs tab by click on ‘Jobs’ in the sidebar, under the Resources section.

Back in May, we announced the release of the Framework v2 API. After helping customers transition to the new Framework over the last 6 months, we're finally removing outdated methods from the SDK with release 0.1.0.

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

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.

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.

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.

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.

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