Defining Features

In this section, you will learn how to define and manage batch, stream, and on-demand feature pipelines using Tecton's feature engineering framework.

Tecton allows for defining features in SQL, PySpark, SnowPark, or Python, subsequently managing the orchestration and maintenance of the created data pipelines. This includes batch, streaming, and real-time pipelines, encapsulating the complete lifecycle of machine learning features.

🗃️ Data Sources

3 items

📄️ Entities

An entity is an object that can be modeled and that has features associated with

🗃️ Feature Views

7 items

📄️ Feature Tables

Available for Tecton on Databricks or EMR. Coming to Tecton on Snowflake in a

📄️ Feature Services

Feature Services group features from

📄️ Example Feature Repos

Check out these sample Tecton feature repositories for end-to-end examples and