Feature stores · Databricks

Databricks Feature Store

Feature registry and serving layer built into Databricks Unity Catalog, using Delta tables as feature tables.

Visit Databricks Feature Store Alternatives

Databricks Feature Store is not a separate product but a capability of the Databricks platform: since Unity Catalog became the feature store, any Delta table with a primary key can act as a feature table, giving it built-in lineage, governance and cross-workspace sharing for free within a Databricks workspace. Feature Engineering client libraries let teams create, read and write feature tables, train models against point-in-time-correct feature data, and publish tables to an online store for low-latency serving, all integrated with MLflow for tracking and Spark Structured Streaming for continuous updates. Because it sits inside Unity Catalog, it inherits Databricks' existing access controls rather than requiring a separate permission system. It is only available to organizations already running Databricks, and it is billed as ordinary Databricks compute and storage rather than as a distinct line item.

At a glance

Vendor Databricks
Pricing model Usage-based
Free tier —
Deployment Cloud
Open source No
Best for Teams already standardized on Databricks who want feature governance without adopting a separate tool.

Pricing

Not sold separately: usage is billed as Databricks compute (DBUs) and Unity Catalog storage within an existing Databricks account.

Pricing has not been verified yet — see the vendor's site.

Features

  • Any Unity Catalog Delta table with a primary key can serve as a feature table
  • Point-in-time-correct joins for training data
  • Online store publishing for low-latency serving
  • Built-in lineage and governance via Unity Catalog
  • Cross-workspace feature sharing and discovery
  • MLflow integration for training and tracking
  • Spark Structured Streaming for continuous feature updates

Integrations

Profile last reviewed September 21, 2026

Head to head

Alternatives

Chalk Real-time feature platform that computes and serves ML features from Python definitions, deployed inside the customer's own cloud. Amazon SageMaker Feature Store Fully managed feature repository inside SageMaker that stores, discovers and serves ML features for training and inference. Vertex AI Feature Store Managed feature-serving module inside Vertex AI that serves ML features from BigQuery data for training and low-latency inference.

Databricks Feature Store in the index now

Terms to know

Machine learning Building models that learn patterns from data to make predictions or decisions, rather than following hand-coded rules. Data lakehouse A storage architecture that adds warehouse-style structure and transactions directly on top of low-cost data lake storage. Data governance The policies, roles and processes that determine how data is defined, owned, accessed and kept compliant. Feature engineering Creating, transforming or selecting the input variables a model is trained on, to make the underlying pattern easier for it to learn. Feature store A centralized system that stores, versions and serves machine learning features so training and live prediction use identical definitions.

Related guides

How to choose a feature store The real choice is whether your cloud or warehouse already has one built in, and whether you need sub-millisecond serving or just a shared registry.