Make Data-Driven Decisions with Warehouse Native Experimentation

Product and experimentation teams need confidence in their data when making high-impact product decisions. Today, experiment results often require copying behavioral data into external systems, which creates delays, security risks, and black-box calculations that are difficult to trust or validate.

Warehouse Native Experimentation keeps experiment data directly in your data warehouse, enabling you to analyze results with full transparency and governance control.

With Warehouse Native Experimentation, you can:

• Run experiments without exporting data
• Use transparent SQL logic that you control
• Maintain alignment with internal data models
• Accelerate experimentation without depending on streaming data pipelines

Why Warehouse Native Experimentation matters today

Product velocity has become a competitive differentiator, but experimentation often lags behind. AI-accelerated development means teams are shipping code faster than ever, while maintaining confidence in data-driven decisions is becoming increasingly challenging.

Modern teams face increasing pressure to move faster while reducing operational costs, reducing risk when launching high-impact features, maintaining strict data compliance and governance, and aligning product decisions with reliable, shared business metrics.

Executives are recognizing that sustainable velocity requires trustworthy insights. According to the 2025 State of AI in Software Engineering report, 81% of engineering leaders surveyed agreed that:

“Purpose-built platforms that automate the end-to-end SDLC will be far more valuable than solutions that target just one specific task in the future.”

At the same time, investments in data warehouses such as Snowflake and Amazon Redshift have increased. These platforms have become the trusted source of truth for customer behavior, financial reporting, and operational metrics.

This shift creates a new expectation where experiments must run where data already lives, results must be fully transparent to data stakeholders, and insights must be trustworthy from the get-go.

Warehouse Native Experimentation enables teams to scale experimentation without relying on streaming data pipelines, vendor lock-in, or black-box calculations, as trust and speed are now critical to business success.

Experiment where your data lives

Warehouse Native Experimentation integrates with Snowflake and Amazon Redshift, allowing you to analyze assignments and events within your data warehouse.

Because all queries run inside your warehouse, you benefit from full visibility into data schemas and transformation logic, higher trust in experiment outcomes, and the ability to validate, troubleshoot, and customize queries.

When Warehouse Native experiment results are generated from the same source of truth for your organization, decision-making becomes faster and more confident.

Create metrics that reflect your business

Metrics define success, and Warehouse Native Experimentation enables teams to define them using data that already adheres to internal governance rules. You can build metrics using existing warehouse tables, reuse them across multiple experiments, and include guardrail metrics (such as latency, revenue, or stability) to ensure consistency and accuracy. As experimentation needs evolve, metrics evolve with them, without duplicate data definitions.

Experiments generate value when success metrics represent business reality. By codifying business logic into metrics, you can monitor the performance of what matters to your business, such as checkout conversion based on purchase events, average page load time as a performance guardrail, and revenue per user associated with e-commerce goals.

Understand experiment impact with transparent results

Once you've defined your metrics, Warehouse Native Experimentation automatically computes results on a daily recalculation or manual refresh and provides clear statistical significance indicators.

Because every result is generated with SQL that you can view in your data warehouse, teams can validate transformations, debug anomalies, and collaborate with data stakeholders. When everyone, from product to data science, can inspect the results, everyone trusts the decision.

Set up Warehouse Native Experimentation

Warehouse Native Experimentation requires connecting your data warehouse and ensuring your experiment and event data are ready for analysis. Warehouse Native Experimentation does not require streaming or ingestion; Harness FME reads directly from assignment and metric source tables.

To get started:

1. Connect your data warehouse to Harness FME. Warehouse Native Experimentation requires the ability to read behavioral event and assignment tables, write results into a dedicated Harness schema, and run scheduled query jobs.
2. Prepare your data model. In your data warehouse, assignment source tables track who was exposed to which variant, ensuring that users are correctly mapped to treatments and environments. Metric source tables, on the other hand, contain event-level data used in metric definitions, ensuring that analyses are grounded in a consistent, verifiable reality.
3. Configure sources in Harness FME. Assignment sources define the exposure table structure and mappings, while metric sources define the event structure and metadata context. This ensures experiment analysis aligns with your warehouse schemas.
4. Define metrics and create experiments. Once your data warehouse is connected, you can add key metrics and guardrail metrics, run experiments, and view the latest results in Harness FME.

From setting up Warehouse Native Experimentation to accessing your first Warehouse Native experiment result, organizations can efficiently move from raw data to validated insights, without building data pipelines.

Start running Warehouse Native experiments today

Warehouse Native Experimentation is ideal for organizations that already capture behavioral data in their warehouse, want experimentation without data exporting, and value transparency, governance, and flexibility in metrics.

Whether you're optimizing checkout or testing a new onboarding experience, Warehouse Native Experimentation enables you to make informed decisions, powered by the data sources your business already trusts.

Looking ahead, Harness FME will extend these workflows toward a shift-left approach, bringing experimentation closer to the release process with data checks in CI/CD pipelines, Harness RBAC permissioning, and policy-as-code governance. This alignment ensures product, experimentation, and engineering teams can release faster while maintaining confidence and compliance in every change.

To start running experiments in a supported data warehouse, see the Warehouse Native Experimentation documentation. If you're brand new to Harness FME, sign up for a free trial today.