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New Features

New Aggregations

Tecton 0.7 introduces support for two new built-in aggregations: percentile and count distinct, which help customers define more flexible and performant feature transformations.

Complex Data Types

This release introduces support for new complex data types: Maps, Structs, and multi-dimensional (a.k.a. nested) Arrays. These help customers create more ergonomic and performant feature definitions.

Enhanced Python Environments for On-Demand Feature Views (Public Preview)

On-Demand transformations can now be configured to run in specified Python environments which enable customers to leverage common Data Science packages when defining feature logic.

Stream Ingest API (Public Preview)

The new Tecton Stream Ingest API provides a simple endpoint to ingest existing real-time feature data, raw data streams, or data from internal services into the Tecton Feature Platform.

Changes, enhancements and resolved issues

Support for Databricks Unity Catalog

The new UnityConfig option enables customers to connect to data sources managed by Unity Catalog, Databricks’ new unified data governance solution. This gives Databricks customers a centralized interface for data assets, fine-grained access control, data lineage, improved data sharing, and other new capabilities. See the documentation for instructions.

Improvements to Manually-Triggered Materialization

Tecton 0.7 introduces the new manual_trigger_backfill_end_time parameter on Feature Views configured for manual materialization — this will automatically backfill the Feature View until the specified timestamp. See the documentation for more details.

New CLI Capabilities

Tecton’s CLI now supports autocompleting commands (by pressing tab). Run tecton completion -h to get started and see the documentation for more details.

Users can now also use the CLI to invite users and manage ACL roles in bulk. Run tecton user invite -h or tecton access-control assign-role -h for instructions.

High-Uptime Stream Updates

This release significantly improves uptime for streaming clusters during routine maintenance and updates, helping ensure that streaming features stay up-to-date.

Unit Testing Improvements

Tecton’s unit testing framework now supports testing feature retrieval. Users can pass mock data into get_historical_features() with the mock_inputs parameter and then test the resulting outputs.

The run() method now also supports unit testing via the mock_inputs parameter. See the documentation for more details.

Minor Feature View parameter changes

Tecton allows customers to specify a ttl in Feature View definitions, which determines 1) how long features will live in the online store and 2) how far to “look back” relative a training event’s timestamp when generating offline training data. In Tecton 0.7, ttl is set to None by default. When a Feature View has ttl unspecified or set to None, 1) feature data will not expire from the online store and 2) the “look back” limit for offline training data generation will be the feature start time.

In addition, some Feature View tags and parameters were added, renamed, deprecated, or removed as part of 0.7. Please review the Upgrade Guide for more details.

Upgrading to 0.7

See the Upgrade Guide for instructions and details on all breaking and non-breaking changes.

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