What is a feature store?

A feature store is a centralized data repository and management system for machine learning (ML) features. In essence, it is a dedicated place where features, which are the input variables to ML models, are stored, curated, and made available for both model training and model serving. The feature store sits in an organization’s data architecture to change raw data from various sources into engineered features that can be used consistently in ML pipelines. By acting as a single source of truth for features, it bridges the gap between data science experimentation and production ML deployments, so models have access to the same features used during training when they are making predictions.

Why are feature stores needed?

Feature stores have emerged to address several challenges in using ML at scale. Data scientists spend most of their time (sometimes cited as ~80%) on data cleaning and feature engineering rather than actual model development. Without a feature store, teams may repeatedly compute or execute the same features for multiple projects, leading to duplicated effort, inconsistent definitions, and higher resource costs. 

A feature store tackles these problems by providing a central platform to keep features so they can be discovered and shared across projects and teams. It also helps enforce consistency. Because features used to train a model are the same as those provided to the model in production, this prevents discrepancies and helps models behave as expected when deployed. In short, feature stores make ML development more efficient, collaborative, and reliable.

Key benefits of feature stores

Feature stores have several traits in common. 

Reusability and efficiency

Multiple models and teams can use features computed once, which reduces duplication of effort.  This saves time and computation, as others can discover and use one team’s engineered feature.

Standardization and governance

Feature stores enforce consistent definitions and computation logic for features across the organization. They act as a single source of truth with documentation of how each feature is produced, which improves data governance and compliance while reducing errors.

Consistency between training and serving

By serving the same feature values to models in production that were used during model training, feature stores reduce the likelihood of discrepancies (known as training–serving skew) between offline training data and online inference data. This leads to more reliable model performance in real-world use.

Low-latency access for real-time machine lea

Feature stores help retrieve feature values faster for live predictions. They typically include an optimized online store that can look up feature vectors by entity, such as a user ID, with millisecond-level latency, so models can make real-time predictions using fresh data. This is critical for production systems such as fraud detection or recommendation engines that require real-time decisions.

Integration of batch and streaming data

Feature stores can ingest and combine data from multiple sources, such as historical data lakes, warehouses, and streaming events. They handle both large-scale batch datasets and fast streaming updates, so features are generated from the most up-to-date information as well as long-term historical facts. This means models have a rich context of both long-term user behavior and real-time events when making predictions.

Jump into our Spark feature-engineering tutorial to see step-by-step how Aerospike changes raw data into high-quality features at scale.

Architecture of a feature store

The typical feature store architecture consists of several components working together within the ML pipeline. At a high level, a feature store maintains two classes of data storage along with pipelines and metadata: an offline store for bulk feature data, such as historical or batch-computed features, and an online store for real-time feature serving. It also includes data ingestion and transformation processes and a feature registry that stores metadata to keep track of feature definitions and versions. Finally, it provides query or API interfaces for model training code and serving applications to retrieve features on demand.

Offline vs. online stores

Feature stores typically support both offline and online features. Here is the distinction.

Offline feature store

This is the repository for historical feature data and large-scale datasets. The offline store contains comprehensive feature values accumulated over time, often appended rather than updated, which builds training datasets and performs batch scoring or analytics. It is designed for high throughput and scale, rather than low latency. 

In practice, offline feature data might reside in data lakes or warehouses, such as files on S3 or BigQuery, Hive tables, and so on, where it can store terabytes of feature history. The offline store creates time-aware training sets, such as pulling a snapshot of features as of a certain past date for a point-in-time correct training dataset.

Online feature store

This is a low-latency database optimized for fast lookups of the latest feature values for a given entity, such as a user, product, or device. The online store typically holds only the most current feature data or recent slices of data needed for live predictions, sacrificing depth of history for speed. It supports real-time inference by helping model servers fetch features (often via a key-value access pattern) in milliseconds. 

The online store is often built on high-performance NoSQL or in-memory databases to meet strict latency requirements. For example, a feature store might use a key-value store or a distributed database to serve features for an API that must decide in real time if a transaction is fraudulent, using the most recent customer activity features.

Typically, data flows from ingestion pipelines into both stores. New data, such as streaming events, updates the online store for immediate use and also gets appended to the offline store for future training. Periodic backfills or syncs from the online store to the offline store help keep the offline data updated and reflective of production reality, helping to mitigate data drift. In some cases, data can also flow in the other direction. Offline computed aggregates or historical features may be pushed to the online store to enrich real-time data with longer-term context.

Feature ingestion and transformation

Raw data from various sources must be ingested and transformed into features to populate the feature store. Feature stores integrate with batch processing frameworks and streaming platforms to automate this feature engineering pipeline. For example, they often use distributed data engines such as Spark for large-scale batch feature computations, and streaming systems such as Kafka or Flink for real-time feature updates.

Ingestion pipelines handle tasks such as joining data from multiple sources, applying transformations or aggregations, and computing feature values on a schedule or in response to events. The goal is to produce consistent features and save them in the offline/online stores with little manual effort. This automated pipeline aspect means new data continually feeds into the feature store, keeping features fresh and models accurate.

Feature registry and metadata

An important but sometimes overlooked component is the feature registry, or megastore. This catalog stores metadata about each feature in the feature store, such as feature name, description, data lineage (or how it’s computed), creation timestamp, and versioning information if features are updated or changed over time. 

The registry helps data scientists find features that already exist and understand their definitions before engineering new ones. It also helps them use the features correctly, such as knowing for which models or uses a feature is approved, and it helps protect data by controlling access to sensitive features. 

Documenting features and where they come from helps staff trust and reuse the feature registry, so teams don’t create duplicate or inconsistent features. In essence, this metadata layer turns the feature store into a collaborative environment for feature engineering, where teams can share and improve upon each other's work.

Feature serving interface

Feature stores have convenient serving interfaces for actually using the stored features in practice. Typically, model training code queries the offline store, often through SQL or DataFrame APIs, to build training sets. Online applications or model inference services query the online store via a low-latency key lookup or REST API to fetch features for live predictions. 

Many feature stores offer SDKs or RESTful endpoints so a client can request feature values by specifying an entity or group of entities and feature names and receiving the feature vector needed for a model. This abstraction makes it easier to integrate the feature store into ML workflows because model developers don't need to know details of where or how the data is stored, only how to request the features they require. 

A well-designed feature store makes retrieval APIs fast and easy to use, so adding a new feature to a model or doing a lookup in production is straightforward. This serving layer, combined with the online store’s speed, helps feature stores support real-time AI applications more easily.

Follow the Spark model-training tutorial to train and serve ML models with features stored in Aerospike’s high-performance database.

Examples of feature store platforms

Large technology companies that built in-house solutions popularized the concept of feature stores. Today, more feature store platforms are available. Uber was one of the pioneers with its proprietary Michelangelo platform, and Airbnb introduced an internal feature store named Zipline. These early systems demonstrated the value of centralizing features for large-scale ML projects. 

Since then, several open-source feature store projects have appeared. For example, Feast, initially developed by Gojek and later adopted by Tecton, and Hopsworks, by Logical Clocks, are widely used open-source feature stores. Cloud vendors also offer turnkey feature store capabilities integrated into their ML platforms, such as Amazon SageMaker Feature Store, Google Cloud Vertex AI Feature Store, and Databricks Feature Store. 

Organizations looking to adopt a feature store generally have three approaches: build, buy, or open source. 

Building a custom feature store in-house is the most flexible way to tailor it to specific needs, but it requires substantial engineering effort and expertise. 

Buying a managed feature store service or using a cloud solution can help you adopt it faster with less overhead, but it reduces control. Open source feature stores strike a middle ground, providing community-driven solutions that organizations can host and customize themselves. Each option has tradeoffs in cost, maintenance, and integration effort. Because feature stores are more popular, more tools and platforms for them are available. 

Challenges and considerations

Implementing a feature store in practice is not without challenges. By introducing a new important piece of infrastructure, teams should be aware of several considerations:

Operational complexity

A feature store requires ongoing maintenance and upkeep. Feature definitions and data pipelines evolve as new data sources arrive and models’ needs change. So, the platform must be actively managed to handle schema changes, backfill data, and scale storage and throughput.

Integration with existing systems

Integrating a feature store into an existing data ecosystem can be complex. It needs to connect with data lakes, databases, streaming sources, and ML workflows. Setting up data pipelines and possibly re-engineering some legacy pipelines is often necessary, which is a lot of work. Organizations should make sure infrastructure, such as messaging systems and extract-transform-load processes, work smoothly with the feature store.

Performance at scale

Feature stores deal with large volumes of data and high query rates, especially in real-time serving scenarios. If not properly optimized, they degrade performance. It’s important to choose scalable storage engines and to tune the system with indexing, caching, and data partitioning for low latency and high throughput as the number of features and requests grows. Another design challenge is keeping offline and online stores in sync and consistent across distributed environments. 

Data quality and validation

Because the feature store becomes the central hub for model inputs, the data has to be good. Teams often implement validation rules and monitoring within the feature pipeline to catch anomalies, prevent feature drift, and avoid serving corrupt or stale features to models. Additionally, staff need to avoid using future data in training by mistake in the offline store, which is called feature leakage, by constructing training sets with correct point-in-time logic. Establishing these practices and tools is an important consideration when deploying a feature store, though the feature store concept itself supports these needs by design.

Despite these challenges, the consensus is that feature stores have become a cornerstone of MLOps in today’s enterprises. When designed and used properly, the benefits far outweigh the overhead for organizations dealing with complex, data-rich ML applications.

Feature stores and Aerospike

Feature stores have become an important component of today’s ML pipeline, and implementing them effectively requires a data platform that can keep up with real-world demands. Aerospike’s real-time NoSQL database provides the speed, scale, and reliability needed for both feature store training and inference. In fact, Aerospike is designed to meet the performance and scalability requirements of an online feature store, delivering sub-millisecond access to features even as the dataset grows to billions of records. This high-throughput, low-latency access means live ML models retrieve the latest feature values in milliseconds for real-time predictions without bottlenecks.

Another advantage of using Aerospike for feature stores is its ability to maintain consistency and handle large workloads across distributed environments. Aerospike’s architecture offers horizontal scalability and a small hardware footprint for cost-efficient operations. It supports millions of read/write operations per second and manages terabytes to petabytes of feature data while maintaining high availability and fault tolerance. 

Many organizations have taken advantage of these strengths in production. For example, companies such as Sony Interactive Entertainment and Quantcast use Aerospike-based feature stores because of Aerospike’s low latency, high performance, and robust uptime. With features such as  Cross Datacenter Replication (XDR), Aerospike synchronizes feature data across sites, so the offline store and the online store remain consistent. Aerospike’s flexible design allows it to serve as both the online and offline store by tuning hardware and storage configurations to cover both needs. This unified approach simplifies architecture and eliminates the need to maintain separate storage systems for the two feature stores. 

Aerospike also integrates with the broader data ecosystem, which means it slots into your ML workflow without friction. Through Aerospike Connect modules, the database connects with popular big data and streaming frameworks used in feature engineering. 

For instance, Aerospike provides connectors for Spark, Kafka, Pulsar, and others so that data teams can ingest streaming events or batch-prepared features into the feature store. A data scientist can compute features in a platform like Spark and write them into Aerospike via the Spark connector, then later retrieve those features via Aerospike’s low-latency APIs during model serving. This interoperability means an Aerospike-backed feature store works with existing tools for data processing, analytics, and model deployment, bridging the gap between offline feature engineering and online inference. 

In addition, Aerospike’s rich data model, which includes key-value records and document-style collections, supports more sophisticated feature management. Aerospike bins can store complex feature values as collections in maps or lists, contributing to a more reliable, efficient, and scalable feature store.