Feature store: The front-end for all of your AI data pipelines

Enterprise AI is evolving beyond individual models to unified data ecosystems.

As organizations scale their AI initiatives, an exciting opportunity emerges—building a unified data gateway that connects every step of your AI pipeline, from raw data through compute processing to feature catalogs and model serving. This isn't just about managing complexity, it's about creating a foundation that helps accelerate innovation.

One recurring challenge organizations face is that data scientists end up rebuilding the same features over and over again. One team calculates customer lifetime value for a churn prediction model, and 3 months later, another team needs the same calculation for a recommendation engine, but they don't know it already exists. So they rebuild it from scratch, introducing inconsistencies and wasting weeks of development time.

This is the feature reuse problem, and it's costing organizations both time and quality.

What is a feature store?

A feature store is a centralized platform that manages, stores, and serves machine learning features—the input variables that models use to make predictions. Think of it as a data catalog specifically designed for AI—instead of hunting through documentation or asking colleagues, "has anyone calculated monthly purchase velocity before?", data scientists can discover, reuse, and share features across all their machine learning (ML) projects.

But a feature store does more than just catalog features. It solves 3 critical problems:

1. Feature reuse: It allows developers to discover and reuse existing features instead of rebuilding them from scratch
2. Training-serving consistency: It helps enforce consistency, so models use identical feature calculations in training and production (eliminating the dreaded "it worked in my notebook" problem)
3. Operational simplicity: It enables the management of  feature pipelines, versioning, and monitoring through a single interface

Red Hat OpenShift AI includes feature store capability built-in—based on the open source Feast project—as a native component of the platform. No separate installation is required, it's available and can be enabled when your teams are ready to adopt feature-first development practices.

The unified data gateway opportunity

Feast can serve as a single, consistent access layer for all of your AI data pipelines. Built on a proven open source foundation with 6,500+ GitHub stars and 16M+ downloads, Feast connects data sources, compute engines (Ray/Spark), and orchestrators (KFP/Airflow) into a unified catalog, so organizations can build vendor-neutral data.

The pipeline front-end: Simplifying complex AI data workflows

Red Hat OpenShift AI's feature store supports this unified approach:

• Vendor-neutral foundation: Integrates with Spark, Ray, Milvus, Elastic, Postgres, and many other popular databases—you choose your infrastructure
• Complete pipeline visibility: From raw data through feature engineering to model inference
• Hybrid deployment freedom: Runs consistently across on-premises, cloud, and edge environments
• Open source innovation: Built on Feast's proven foundation with over 16 million downloads and used and contributed to by many enterprises including Shopify, NVIDIA, Walmart, and more.

This approach also solves real enterprise challenges. Federal agencies can process sensitive data on-premises while leveraging cloud compute. Financial institutions can meet compliance requirements while maintaining operational flexibility. Manufacturing companies can process data at the edge while connecting to centralized analytics.

The 3-layer architecture: Data, compute, and catalog

Red Hat's approach to AI data management builds on a simple but powerful insight—the best enterprise platforms connect existing infrastructure rather than replacing it. We'll show you how this works in practice through the story of a financial services company adopting feature stores.

Layer 1: Data sources—Meeting your data where it lives

Consider a large bank implementing fraud detection. Their customer data lives in an on-premises Oracle database (regulatory compliance requirements), transaction streams flow through Kafka on AWS (modern real-time processing), and historical patterns sit in a Snowflake data warehouse (analytics team investment from 3 years ago).

Traditional feature store solutions force a choice: migrate everything to the platform, or don't use the feature store at all. This creates a very difficult situation—the Oracle database can't move due to compliance, the team won't abandon their Snowflake investment, and the real-time Kafka pipelines are critical to operations.

Red Hat's feature store solves this through universal data connectivity:

• Connect anywhere: Features can pull from on-premises databases, cloud storage, edge sensors, and streaming platforms—all in the same feature definition
• Preserve investments: The fraud detection team continues using their existing infrastructure without migration costs or operational disruption
• Maintain compliance: Sensitive customer data stays in the compliant on-premises database while the feature store orchestrates governed access

The bank's fraud detection team defines their features once— "customer_transaction_velocity_30d", "account_risk_score", "merchant_category_pattern"—and the feature store handles the complexity of pulling from Oracle, joining with Kafka streams, and enriching with Snowflake history. Data scientists never write another JOIN statement to stitch these sources together.

Layer 2: Compute processing—Flexibility for every workload

Now let's talk about how those features get calculated. The fraud detection team needs to process billions of transactions daily, but different features have different computational needs:

• Simple aggregations (transaction counts) run efficiently in SQL
• Complex pattern detection (behavioral anomalies) requires Spark for distributed processing
• Real-time risk scoring (sub-second latency) needs lightweight streaming compute

Most feature platforms lock you into their preferred compute engine. If you've invested in Spark expertise and infrastructure, you're told to abandon it and learn their proprietary system. If you need Ray for ML-heavy transformations, you're out of luck.

Red Hat's feature store provides compute flexibility:

• Vendor-neutral engines: Native support for Ray and Spark, plus the ability to bring your own compute framework (Spark, Ray, etc.)
• Open standards: Features defined using standard Python and SQL, not proprietary DSLs that create lock-in

The fraud detection team runs their simple aggregations in Postgres (already deployed), executes complex behavioral models in their existing Spark cluster (preserving years of infrastructure investment), and deploys real-time scoring engines at branch locations for instant fraud detection. Same feature definitions, different compute strategies based on business requirements.

Layer 3: Unified catalog—Your single interface to all features

This is where the challenges start. The fraud detection team has defined 50+ features pulling from 3 data sources and executing on 2 compute platforms. Without a unified catalog, here's what happens:

• Data scientists waste hours hunting through Git repos, Jupyter notebooks, and team knowledge trying to figure out if anyone has already built a monthly transaction velocity calculator
• When they do find a feature, they discover it's incompatible—different column names, different timestamps, different aggregation windows
• Production engineers struggle to understand feature dependencies—which features rely on which data sources and compute jobs?
• Compliance officers can't answer the question, "who has access to sensitive customer features?"

The unified catalog (Feast) solves all of this:

• Single interface: Data scientists discover all 50 features through 1 search interface—no hunting through repos or asking in Slack
• Complete pipeline visibility: Each feature shows exactly where data comes from, what compute it requires, and which models consume it
• Enterprise-ready governance: Built-in Role-Based Access Control (RBAC) means only authorized teams have access to sensitive features, complete audit trails track every access, and approval workflows enforce production deployment standards

Here's what this looks like for 2 different users:

Admin workflow (platform team):

1. Enable feature store: In the OpenShift AI dashboard, navigate to feature store settings and enable the component (built-in, no separate installation)
2. Configure permissions: Define which data science teams can create features, which can only consume features, and which data sources are accessible to each team
3. Monitor operations: Dashboard shows feature pipeline health, resource utilization, and data freshness

Data scientist workflow:

1. Discover features: Search the feature catalog for "transaction" - find 12 existing features including "customer_transaction_velocity_30d" built by the fraud team last quarter
2. Understand context: Click into the feature to see data sources (Kafka transactions + Oracle customers), compute requirements (Spark job, runs daily), and example usage code
3. Reuse in new model: Copy the feature definition into their recommendation engine project and get the same calculation logic and consistency between fraud detection and recommendations
4. Iterate quickly: Launch pre-integrated Jupyter notebooks directly from the feature catalog with authentication already configured

The result: What used to take 3 days of research, 5 Slack conversations, and debugging inconsistent calculations now takes 10 minutes. And when the fraud team improves their transaction velocity calculation, all downstream models automatically benefit from the enhancement.

This is the compounding value of a unified catalog—every feature created makes the entire organization's AI development faster, more reliable, and more consistent.

The business impact: From tactical tool to strategic platform

This 3-layer architecture transforms feature stores from a tactical component into a strategic data gateway that orchestrates all AI data consumption. Instead of managing separate pipelines for different AI initiatives, you establish a single, governed entry point that serves traditional ML models, gen AI applications, and advanced hybrid workflows.

The business impact is transformative:

• Faster innovation: Data scientists discover and reuse features across projects instead of rebuilding from scratch, reducing time-to-market
• Stronger governance: Single point of control for data access policies, audit trails, and compliance requirements across all AI initiatives
• Better economics: Shared infrastructure and reusable assets reduce per-project costs while improving quality
• Strategic flexibility: Platform-independent architecture that adapts as your technology stack evolves, preserving your ability to innovate

As AI becomes central to business operations, early adopters of vendor-neutral data infrastructure gain sustainable competitive advantage in innovation velocity and operational excellence.

Conclusion: Building your AI data foundation for success

Red Hat OpenShift AI's feature store capability represents more than a feature management solution—it's your platform for building a vendor-neutral AI data ecosystem that helps accelerate innovation, optimize operations, and preserve strategic flexibility.

Your data strategy enables your AI future—build on a foundation that grows with your organization's capabilities while preserving the flexibility to innovate.

Get started

Ready to explore the feature store approach for your enterprise?

• Start a trial: Red Hat AI product trial
• Try Feast examples: Community demos and tutorials
• Navigate your AI journey with Red Hat: AI consulting services
• Contact the team: jzarecki@redhat.com
• Explore the code: Feast GitHub Repository
• Learn more: OpenShift AI Documentation