Feature Store

Real-Time Feature Store for ML

Compute and serve ML features in real-time using SQL. RisingWave continuously updates feature values from streaming data — enabling fresh predictions, real-time recommendations, and fraud detection without feature serving infrastructure.

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Sub-Second

Feature Freshness

Materialized views update incrementally as events arrive — features reflect the latest data within milliseconds

SQL

Feature Engineering

Define feature transformations with standard SQL — no Spark jobs or custom Python pipelines required

PostgreSQL

Serving Interface

Query features via any PostgreSQL client — JDBC, psycopg2, or direct SQL from your ML serving framework

Multi-Source

Feature Joins

Join Kafka streams with CDC tables and dimension data in a single SQL query for rich feature vectors

Why Real-Time

Why do ML teams need real-time feature stores?

ML models make better predictions with fresher data. Batch feature pipelines introduce hours of staleness, causing fraud to slip through, recommendations to lag behind user intent, and pricing models to miss market shifts. Real-time feature stores close this freshness gap by computing and serving features as events happen.

Factor	Batch Feature Store	RisingWave
Feature Freshness	Hours (batch schedule)	Sub-second (streaming)
Training-Serving Skew	High (different pipelines)	None (unified system)
Infrastructure	ETL + Store + Serving API	Single SQL system
Time to New Feature	Days to weeks	Minutes (SQL iteration)

→Eliminate training-serving skew by using the same streaming SQL for both batch and real-time features
→Reduce end-to-end feature latency from hours to sub-second
→Avoid separate ETL pipelines, feature stores, and serving APIs
→Enable data scientists to iterate on features in minutes using SQL

Use Cases

What ML use cases benefit from real-time features?

Any ML application where prediction quality depends on data freshness benefits from real-time features. Fraud detection, recommendation engines, dynamic pricing, and risk scoring all see measurable accuracy improvements when features reflect the latest user behavior and system state rather than stale batch snapshots.

Fraud Detection

Compute transaction velocity, spending pattern deviations, and device fingerprint changes in real time to catch fraud as it happens

Recommendation Engines

Update user preference features from clickstream and purchase events, enabling recommendations that reflect current browsing intent

Dynamic Pricing

Maintain real-time supply-demand ratios, competitor price signals, and inventory levels as features for pricing optimization models

Credit Risk Scoring

Combine real-time transaction features with historical credit data for instant risk assessment that adapts to changing borrower behavior

How It Works

How does RisingWave serve as a real-time feature store?

RisingWave combines feature computation and serving in a single system. Define features as materialized views over streaming sources using SQL. RisingWave incrementally maintains these features with sub-second freshness and serves them through a PostgreSQL-compatible interface — no separate ETL pipelines or serving infrastructure required.

→Define feature transformations as SQL materialized views over Kafka, CDC, or other streaming sources
→Automatically maintain features with sub-second freshness as new events arrive
→Serve features via PostgreSQL-compatible queries from any ML serving framework
→Join real-time event streams with historical dimension tables for rich feature vectors
→Scale feature computation horizontally with RisingWave's distributed architecture

Frequently Asked Questions

How does RisingWave compare to Feast or Tecton?

Can I compute features from multiple data sources?

How fresh are the features in RisingWave?

Can I serve features directly to my ML models via SQL?

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