> For the complete documentation index, see [llms.txt](https://developer.harness.io/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://developer.harness.io/feature-management-experimentation/use-fme/warehouse-native-experimentation.md).

# Warehouse Native Experimentation

### Overview <a href="#overview" id="overview"></a>

Warehouse Native enables [experimentation](/feature-management-experimentation/use-fme/experimentation/setup/index.md) workflows, from targeting and assignment to analysis, and provides a statistical engine for analyzing existing experiments with measurement tools in Harness Feature Management & Experimentation (FME).

### How Warehouse Native works <a href="#how-warehouse-native-works" id="how-warehouse-native-works"></a>

Warehouse Native runs experimentation jobs directly in your [data warehouse](#user-content-fn-1)[^1] by using your existing data to calculate metrics and enrich experiment analyses.

![](/files/jt2MEc5zglelJPphwUv9)

The data model is designed around two two primary types of data: **assignment data** and **performance/behavioral data**, which power the FME statistical engine in your warehouse.

Key components include:

* **Assignment data**: Tracks user or entity assignments to experiments. This includes metadata about the experiment.
* **Performance and behavioral data**: Captures metrics, events, and user behavior relevant to the experiment.
* **Experiment metadata**: Contains definitions for experiments, including the experiment ID, name, start/end dates, traffic allocation, and grouping logic.
* **Metric definitions**: Defines how metrics are computed in the warehouse, including aggregation logic and denominators. These definitions ensure analyses are standardized across experiments.

#### Cloud Experimentation <a href="#cloud-experimentation" id="cloud-experimentation"></a>

Cloud Experiments are executed and analyzed within Harness FME, which collects feature flag impressions and performance data from your application and integrations. For more information, see the [Cloud Experimentation documentation](/feature-management-experimentation/use-fme/experimentation.md).

```mermaid
flowchart LR
  %% Customer infrastructure
  subgraph CI["Customer Infrastructure"]
    direction TB
    subgraph APP["Your Application"]
      FME["FME SDK"]
      style FME fill:#9b5de5,stroke:#9b5de5,color:#fff
    end

    integrations["Integrations including Google Analytics, Segment, Sentry, mParticle, Amplitude, and Amazon S3"]
    style integrations fill:none,stroke:none,color:#fff
  end
  style CI fill:#8110B5,stroke:#8110B5,color:#fff

  %% Harness FME System
  subgraph HFM["Harness FME"]
    direction TB

    %% Horizontal input boxes without a subgraph
    FF["FME Feature Flags"]
    PD["Performance and behavioral data"]
    style FF fill:#9b5de5,stroke:#9b5de5,color:#fff
    style PD fill:#9b5de5,stroke:#9b5de5,color:#fff

    AE["FME Attribution Engine"]
    style AE fill:#9b5de5,stroke:#9b5de5,color:#fff

    %% Connect inputs to Attribution Engine
    FF --> AE
    PD --> AE
  end
  style HFM fill:#8110B5,stroke:#8110B5,color:#fff

  %% Arrows from Customer Infra to input boxes
  CI -- "Feature flag impression data" --> FF
  CI -- "Performance and additional event data" --> PD
```

#### Warehouse Native <a href="#warehouse-native" id="warehouse-native"></a>

Warehouse Native Experiments are executed directly in your data warehouse, leveraging assignment and behavioral data from Harness FME to calculate metrics and run statistical analyses at scale.

```mermaid
flowchart LR
  subgraph DW["Data Warehouse"]
    style DW fill:#8110B5,stroke:#8110B5,color:#fff
    direction TB
    AF["Assignment and FME feature flag data"]
    PB["Performance and behavioral data"]
    AE["FME Attribution Engine"]
    style AF fill:#9b5de5,stroke:#9b5de5,color:#fff
    style PB fill:#9b5de5,stroke:#9b5de5,color:#fff
    style AE fill:#9b5de5,stroke:#9b5de5,color:#fff
  end

  subgraph HFME[" "]
    direction TB
    HFM["Harness FME"]
    PAD1[" "]:::invisible
    PAD2[" "]:::invisible
  end

  classDef invisible fill:none,stroke:none;
  style HFM fill:#8110B5,stroke:#8110B5,color:#fff

  DW --> HFM

```

### Get started <a href="#get-started" id="get-started"></a>

To get started, [connect a data warehouse](/feature-management-experimentation/use-fme/warehouse-native-experimentation/integrations/index.md) and set up [assignment and metric sources](/feature-management-experimentation/use-fme/warehouse-native-experimentation/setup/index.md) to enable Warehouse Native Experimentation in Harness FME.

[^1]: A centralized repository for storing and managing large volumes of structured and semi-structured data. Examples include Snowflake, BigQuery, Redshift, and Databricks.