All about feature stores

# Introducing feature stores

Feature stores they are no longer niche infrastructure, but a key front-end that helps push the boundaries of data pipelines, especially those involving machine learning and other artificial intelligence systems. They have become a trend this year, mainly due to the industry’s shift from experimental building of machine learning models to the need to operationalize scalable AI-based solutions, products and services.

This article gently introduces feature stores, describing their origins, main features, reasons for their current importance, and currently popular tools.

# Tracing the origins and evolution of feature stores

The term “feature store” was coined by Uber in 2017 to simplify what they call the “jungle of data pipelines” and enforce feature management and consistency. As a result, they created a centralized repository to store, share, and reuse features across multiple machine learning models and projects, while maintaining consistency between training and production data.

Shortly thereafter, in 2019, the first third-party provider of an enterprise-level feature store, Tektonwas founded by the same former Uber engineers who co-founded Uber’s internal feature store. Their goal was to bring commercial feature store solutions to the entire enterprise market, and their product launched in 2020. Around the same time, cloud-native feature store solutions appeared on mainstream platforms Amazon Web Services (AWS), Google CloudAND Microsoft Azure. These managed services, typically tightly integrated with appropriate machine learning platforms, have since evolved and matured to the present day.

But what exactly is a feature store? It can be defined as a centralized platform or system where all data features associated not with a single, specific dataset, but with an entire machine learning domain – a set of models under the same overarching business goals – or organization are defined and managed. In a function store, functions are described declaratively by specifying their business semantics, source data, transformation logic, associated metadata, and their availability for offline training and online model inference or sharing.

Feature stores can therefore be thought of as: the only source of truth for a function in a domain (usually business-oriented). Feature reuse, enforcing consistency between model training and sharing, and the fundamentals of managing, monitoring, and scaling machine learning operations are additional hallmarks – characteristicsif you prefer – current feature store systems.

In a function store, functions are described declaratively by specifying their business semantics, source data, transformation logic, associated metadata, and their availability for offline training and online model inference or sharing.

# Understanding feature stores with an example

To better understand the key concepts and features associated with feature stores, let’s consider an example scenario of an e-commerce company that is building a set of machine learning models for fraud detection.

With the support of the company’s trusted cloud service provider, a feature store was designed to define the appropriate features exposed to fraud detection and management models. Such relevant characteristics include, but are not circumscribed to: the number of transactions initiated by the user in the last 24 hours, the average transaction amount over the last week, the number of different payment methods used by the user in the last month, and the time since the user’s last transaction.

Now let’s take a closer look at one of these features to better understand what the feature store “has to say” about it. Let’s consider an example function user_transaction_count_24h: :

• Business semantics: This feature describes the number of transactions initiated in the last 24 hours for a given user.
• Source data: The function is retrieved from the data in the file transactions table – an event type table containing columns for user_id, transaction_timestampsAND status.
• Transformation logic: To get this, you need to count the transactions from initiated status grouped by distinct user_id is calculated over a rolling window of 24 hours.
• Related metadata:
• Owner: Machine Learning Fraud Team.
• Type: integer.
• Window: 24h.
• Freshness SLA (Service Level Agreement): 5 minutes.
• Availability: Available for both offline training and online support.

Importantly, the freshness SLA refers to how fresh a feature value should be to be considered valid for apply by the model. This is a feature stores mechanism that helps ensure reliability and consistency in the behavior of machine learning models.

Sample feature specs in the feature store | Photo by the author

# Discover the buzz and popular tools in the 2026 feature store

There are many reasons why feature stores, while not a recent paradigm, have become an significant trend in data analytics and artificial intelligence today. Here are some of them:

• With the rise of agent-based AI, the value of feature stores has increased because they provide the high-quality, real-time data features needed for cutting-edge AI agents to autonomously perform sophisticated, multi-step tasks.
• Organizations are increasingly recognizing the importance of data infrastructure rather than machine learning models built in isolation. Feature stores are the glue and foundation that helps them make this change.
• Feature stores assist avoid duplication of effort among data engineering teams, making reusing curated and production-ready features the recent norm.
• Feature stores are adapting to recent, more stringent AI regulations on aspects such as centralization and alignment with transparency standards.
• For domain-specific goals and KPIs such as hyper-personalization (in sectors like retail), feature stores push the boundaries of real-time analytics.
• On the cost side, function stores assist manage rising infrastructure costs and performance by preventing redundant data processing and ultimately reducing computational load.

The most popular feature store tools used by many companies to leverage current AI applications include:

1. Holiday: An open source store, ideal for teams with sufficient engineering resources and wanting to avoid vendor lock-in.
2. Tecton (data cubes): Recently acquired by Databricks, Tecton is a fully managed, scalable enterprise solution ideal for managing sophisticated data pipelines in real-time.
3. Google Cloud Vertex AI Features Store: Stands out for its integration with Google BigQuery and cutting-edge generative artificial intelligence models.
4. Amazon SageMaker Features Store: Tightly integrated with AWS, it elegantly supports feature search in both batch mode and real-time model inference.

# Final remarks

Feature stores have now gained a lot of popularity in line with the latest advancements in artificial intelligence and the growing organizational needs to keep up with the constant progress and evolving goals and needs. The purpose of this article is to provide a gentle introduction to feature stores, describing what they are, their characteristics, evolution and most significant tools.

Ivan Palomares Carrascosa is a thought leader, writer, speaker and advisor in the fields of Artificial Intelligence, Machine Learning, Deep Learning and LLM. Trains and advises others on the apply of artificial intelligence in the real world.