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  • Introduction
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    • Architecture
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    • Validating historical features with Great Expectations
    • Using Scalable Registry
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  • How-to Guides
    • Running Feast with Snowflake/GCP/AWS
      • Install Feast
      • Create a feature repository
      • Deploy a feature store
      • Build a training dataset
      • Load data into the online store
      • Read features from the online store
      • Scaling Feast
      • Structuring Feature Repos
    • Running Feast in production (e.g. on Kubernetes)
    • Upgrading for Feast 0.20+
    • Customizing Feast
      • Adding a custom batch materialization engine
      • Adding a new offline store
      • Adding a new online store
      • Adding a custom provider
    • Adding or reusing tests
  • Reference
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    • Type System
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    • Offline stores
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      • File
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      • ScyllaDB (contrib)
    • Providers
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      • Amazon Web Services
      • Azure
    • Batch Materialization Engines
      • Bytewax
      • Snowflake
      • AWS Lambda (alpha)
      • Spark (contrib)
    • Feature repository
      • feature_store.yaml
      • .feastignore
    • Feature servers
      • Python feature server
      • [Alpha] Go feature server
      • [Alpha] AWS Lambda feature server
    • [Beta] Web UI
    • [Alpha] On demand feature view
    • [Alpha] Data quality monitoring
    • Feast CLI reference
    • Python API reference
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  • Project
    • Contribution process
    • Development guide
    • Backwards Compatibility Policy
      • Maintainer Docs
    • Versioning policy
    • Release process
    • Feast 0.9 vs Feast 0.10+
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  1. Reference

Batch Materialization Engines

PreviousAzureNextBytewax

Last updated 1 year ago

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Please see for an explanation of batch materialization engines.

Batch Materialization Engine
Snowflake
Bytewax
AWS Lambda (alpha)
Spark (contrib)