Choosing the Right Feature Store: Feast or Tecton?

We've broken down the recording of the talk we gave on October 27, 2022, to help you easily navigate and find the content relevant to you. If you’d like to watch the whole session in one go, you’ll find it at the bottom of this page!

 

Overview of the differences between Feast and Tecton, and when to select one or the other

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Hands-on demos

If you’d like to follow along, here are the GitHub repositories for the Feast demo and Tecton demo.

Creating features with Feast 

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It's important to note that Feast primarily helps in serving features for online inference and offline training, but not in transforming feature values (Feast takes transformed values as input). This is why creating a feature in Feast consists of registering a pre-processed feature, along with its metadata. 

Creating features with Tecton 

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In contrast to Feast, when you create a feature in Tecton, you're actually defining the feature transformation itself. Tecton will manage the data pipeline for you and store the materialized data in online and offline stores, as well as run the backfill for you. 

Managing data pipelines with Feast 

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As mentioned above, Feast takes transformed values as input. This means you'll have to manage your own data pipelines. The typical steps include: 

  • Creating a streaming pipeline (e.g., with Spark)
  • Creating a batch pipeline (e.g., with DBT) 
  • Setting up monitoring to make sure your features are properly sent to Feast and watch out for train / serve skew
  • Setting up a backfill job with Airflow

Managing data pipelines with Tecton 

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Because Tecton handles the feature transformations for you, it removes a lot of the complexity. There’s no need to create pipelines, set up monitoring, or run Airflow jobs yourself—Tecton will create these pipelines for you in the background, and handle monitoring and alerting as soon as you create the feature (as described in the previous step).

Serving features with Feast 

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Both Feast and Tecton allow you to serve features online and create training datasets. They share the same feature retrieval APIs: 

  • 'get_offline_features' to generate a training dataset with point-in-time correct features with labels
  • 'get_online_features' to retrieve the features you need to make a prediction (for instance, to predict whether a transaction is fraudulent).

Serving features with Tecton 

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Similar to Feast, Tecton allows you to serve features online and create training datasets. Tecton has the same feature retrieval APIs as Feast. The main difference is that Tecton manages the online store for you, which includes a layer of monitoring and alerting (for example, if feature values stop coming through and your feature is getting stale, you'll receive an alert that your upstream data source might be down). 

Scaling Considerations

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Feast
requires custom configuration to power production ML at scale. Common considerations include:

  • How to reliably and scalably compute batch and / or streaming features
  • How to reliably and scalably backfill features to the offline / online store
  • How to efficiently retrieve features in a real-time recommender system
  • How to manage online store costs given large data volumes

Tecton is a fully hosted solution that ensures enterprise scalability and reliability, with SLA guarantees. 

Recap

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Use Feast if you:

  • Have the resources and skills to manage your own feature store
  • Need a highly customizable solution
  • Don’t need any help with streaming or real-time data pipelines
  • Need to deploy on-prem, on GCP, or on Azure

 

Use Tecton if you:

  • Want minimal overhead in managing your own feature store
  • Need mission-critical reliability, scalability, and/or support
  • Want to automate batch, streaming, and real-time features
  • Want to collaborate on, share, and re-use features

 

Want to learn more?
Request your personalized demo of Tecton

Feast or Tecton? Watch the whole session

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