Why FeatureMesh?
We believe teams should spend their time creating value with data, not fighting the complexity of integrating it.
Too many data-driven initiatives in BI, ML, or AI fail because connecting and maintaining consistent data is still too hard.
FeatureMesh was created to solve this.
The challenge
In most organizations, non-data-engineers depend on SQL and pipelines to build analyses and features.
Over time, this leads to:
- Business logic buried in scattered, hard-to-maintain queries
- Complex, table-based thinking that slows iteration
- Duplicated and inconsistent definitions across tools
- Fragmented data and disconnected systems
The result: wasted effort, unreliable insights, and limited scalability.
The FeatureMesh approach
FeatureMesh is an abstraction layer that unifies data consumption on top of existing systems.
It introduces a clear separation between data production and data consumption, enabling each team to focus on what they do best.
Data production: Data engineers continue using their preferred tools to build and maintain solid data foundations. They expose clean, reusable data entities as Source Features, the single point of contact with data consumption.
Data consumption: Non-data-engineers define Computed Features using FeatureQL, a high-level, column-based language that abstracts SQL complexity. Logic becomes composable, testable, and reusable, built around semantic entities instead of tables.
Why it matters
FeatureMesh replaces scattered SQL and siloed workflows with unified and consistent features.
| Traditional SQL Approach | FeatureMesh Approach |
|---|---|
| Table-based logic | Entity-centered modeling |
| Scattered and duplicated queries | Reusable, composable features |
| Inconsistent definitions | Centralized, testable features |
| Fragmented data | Unified, consistent registry |
| Separate analytical and operational systems | One language, all use cases |
What you get
Data engineers
- No more one-off requests for custom tables or views that adds to the complexity of the data landscape
- Focus on maintaining reliable, high-quality data sources
Product and business users
- Define logic with simple, column-based formulas, no SQL or pipelines required
- Work independently without waiting for engineering support
Organizations
- Ensure consistent, trustworthy data across teams and use cases
- Simplify collaboration and accelerate time to market