Why FeatureMesh?

FeatureMesh lets you define business logic once and use it everywhere.

Whether you're calculating customer lifetime value, determining pricing, or defining what makes a user 'active', you write it once in FeatureQL and use it in dashboards, APIs, ML models, and production systems.

What is FeatureQL?

FeatureQL is a SQL-based language designed for reusable business logic. It has three key characteristics:

Column-focused (not table-focused): You define individual features (columns) rather than entire tables. Define "customer_lifetime_value" once, reference it anywhere. Dependencies are explicit, lineage is automatic.

Pure transformations: Each feature is a self-contained function with no hidden side effects. This makes features composable, testable, and predictable.

Execution-agnostic: Write your logic once. FeatureQL compiles to native SQL (DuckDB, Trino, BigQuery) for batch analytics or runs in-memory (via DataFusion) for real-time APIs. You define what to compute, not how to execute it.

Problems it solves

Consistent metrics everywhere: Everyone uses the same definition of "active customer" or "monthly recurring revenue". No more meetings about why the dashboard shows different numbers than the experiment analysis.

Reusable business logic: Define your revenue calculation once as a feature. Use it in 50 different queries. When the logic changes, update one place instead of hunting through countless SQL files.

One language for all contexts: The same feature definitions power your dashboards, ML training pipelines, real-time APIs, and operational systems. No more translating between analytics SQL and application code.

Simpler complex queries: Operations on nested data (like aggregating all orders per customer) are natural with FeatureQL's array functions. What takes 100 lines of SQL with multiple CTEs becomes 10 clear lines.

Rapid iteration: Compose existing features into new definitions. Want to test a new customer segmentation? Combine "purchase_frequency", "avg_order_value", and "days_since_signup" into a new segment feature. Deploy in minutes, not hours.

Faster debugging: Features are isolated, testable functions. Debug each feature independently instead of untangling a 500-line SQL query to find where the logic breaks.

Better team collaboration: Data engineers publish verified features. Analysts and scientists reuse them with confidence. New team members become productive immediately instead of learning years of tribal knowledge.

You might not need FeatureMesh

FeatureMesh is built for teams who:

• Use the same business logic across multiple systems (dashboards + APIs, or BI + ML)
• Need metrics to stay consistent across teams and tools
• Deploy business logic to production (not just run analytics queries)

If you're only building dashboards with straightforward SQL, your existing tools probably work fine.