Prepare and Store Data
Feature Store

Manage the entire feature lifecycle, from development to deployment, with a fully managed feature store, allowing you to focus on building and delivering innovative features.

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Key Capabilities


Streamline the creation of feature values through the seamless orchestration and processing of data pipelines. Utilize a unified approach to define transformations across various data sources while maximizing efficiency.


Eliminate the risk of training-serving skew with a sophisticated storage strategy that maintains consistent feature values across offline and online storage. Optimize for large-scale, cost-effective training data retrieval with an offline store, while leveraging an online store for lightning-fast, low-latency data access during online serving.


Experience fast, simple and reliable feature serving. Generate high-quality training data and automatically fill in missing features in your training sets. Protect the integrity of your data by properly managing it across training and serving environments.

Main Benefits

Feature Collaboration

Enables your data scientists and ML engineers to easily collaborate and share features across projects

Training-Serving Skew

Systematically ensures the consistency of online and offline generated features.

Source of Truth

Serves as a single, discoverable source of truth for features used by your production models.

Data Ingestion

Data Warehouse Sourced Features

Data warehouse can serve as the primary source of data for the feature store. The feature store ingests data from the data warehouse, processes it to extract and transform relevant features, and stores the resulting features in a feature store database.

Multiple Data Sources

Ingest and manage features from a variety of different data sources. This can include structured data sources such as databases, unstructured data sources such as logs and text files, and real-time data streams.

Streaming Aggregation

Continuously collect processing data as it is generated directly from the stream (Kafka, Kinesis and Pub/Sub) in order to compute aggregative value (such as a sum, average, or count). This process can be used to perform real-time analytics on streaming data, or to update some running aggregate value as new data arrives.

Feature Extraction

Training API

Users can retrieve point in time data they need for training and testing purposes,
using the Training API which is optimized for time travel and large datasets
The Training API refers to the portion of the feature store that is used to store and manage the historical versions of features that have been computed and stored in the feature store.

Serving API

Real-time serving in a feature store enables the use of the most up-to-date versions of pre-computed features in real-time as they are being generated or updated.
This capability is designed for production use, with the ability to support large scale, high throughput, and low latency. This makes the Serving API ideal for applications that require immediate access to the most current data, such as real-time personalization, fraud detection, and anomaly detection, and many more.

Easily integrate with any data source

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We'll take care of operations - you focus on the science