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Warehouse - Native Experimentation
Make confident product decisions by running experiments directly in your data warehouse, using metrics and SQL you fully control.
Run Experiments Faster
Use your Data Warehouse to run experiments on governed data without waiting for ETL or clunky configuration.
Eliminate ETL Bottlenecks
Analyze where data lives to eliminate bespoke pipelines with the latest data about your customers.
Protect Sensitive Data
Keep PII and behavioral data inside governed environments for compliance and security.
Key Benefits
Experiment where your data lives
Run experiments directly in Snowflake and Amazon Redshift, without exporting behavioral data into external tools. Keep analysis inside your source of truth so data teams and product teams stay aligned.
Trust metrics that match your business
Define metrics using existing warehouse tables and governance rules, then reuse them across experiments. Add guardrail metrics such as latency, revenue, and stability so every rollout is measured against what matters to your business.
Move faster with transparent results
Use clear statistical outputs and SQL you can inspect to understand experiment impact. Validate transformations, debug anomalies, and collaborate easily with data stakeholders to make decisions quickly and confidently.
Experiment where your data lives
Warehouse-Native Experimentation integrates with Snowflake and Amazon Redshift so you can analyze assignments and events directly in your data warehouse.
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Run all experiment queries from inside your warehouse, not an external black box.
Maintain full visibility into data schemas and transformation logic.
Validate and customize queries using your existing SQL tools and workflows.
Keep behavioral data inside governed environments for compliance and security.
Create metrics that reflect your business
Experiments only generate value when success metrics represent business reality. With Warehouse-Native Experimentation, you define metrics using data that already follows your internal governance rules.
Build metrics from existing warehouse tables and views.
Reuse metrics across multiple experiments to ensure consistency.
Include guardrail metrics such as latency, error rate, revenue, or stability.
Evolve metrics over time without duplicating data definitions.
Checkout conversion based on purchase events
Average page load time as a performance guardrail
Revenue per user tied to e-commerce goals or subscription plans
Understand experiment impact with transparent results
Once metrics are defined, Warehouse Native Experimentation automatically computes results on a schedule or on demand, using SQL that runs inside your data warehouse.
Get daily recalculations or manual refreshes of experiment results.
View clear statistical significance indicators and effect sizes.
Inspect the underlying SQL in your warehouse to validate logic.
Collaborate with product, experimentation, and data science teams on the same source of truth.
How Warehouse Native Experimentation works
From connecting your warehouse to viewing your first experiment result, Harness FME guides you through a clear, warehouse-centric workflow.
Connect your data warehouse
Grant Harness FME read access to behavioral event and assignment tables, plus the ability to write results to a dedicated Harness schema and run scheduled query jobs.
Prepare your data model
Metric source tables contain event-level data used in metric definitions. This ensures experiment analyses are grounded in a consistent, verifiable representation of user behavior.
Configure sources in Harness FME
Define assignment sources to model how exposure is stored and mapped, and metric sources to represent event schemas and context. This configuration aligns experiment analysis with your warehouse data model.
Define metrics and create experiments
Add key metrics and guardrails, then create experiments that use these metrics by default. Run experiments, monitor results as they refresh, and feed validated insights back into product roadmaps.
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Frequently Asked Questions
What is Warehouse-Native Experimentation in Harness FME?
Warehouse-Native Experimentation is an extension of Harness Feature Management & Experimentation that runs experiments directly in your data warehouse. It reads from assignment and metric source tables, writes results into a Harness schema, and uses SQL you can inspect so analysis is transparent and auditable.
How is this different from traditional experimentation tools?
Traditional experimentation tools often require copying data into separate systems and rely on black-box calculations. Warehouse-Native Experimentation keeps data in place, uses your existing schemas and governance, and lets you review and customize SQL, so you maintain end-to-end visibility and control.
Do I need streaming data pipelines for Warehouse-Native Experimentation?
No. Warehouse-Native Experimentation does not require streaming or ingestion pipelines. Harness FME reads directly from your warehouse tables, which reduces operational overhead and avoids additional infrastructure.
Which data warehouses are supported?
Warehouse-Native Experimentation supports Snowflake and Amazon Redshift, with additional platforms planned. For the latest list of supported warehouses, refer to the product documentation.
Who owns and manages the metrics definitions?
Your teams define metrics using warehouse data that already follows internal governance rules. Product, experimentation, and data teams can collaborate on shared metric definitions, ensuring that all experiment results map to trusted business KPIs.
Start running Warehouse Native experiments today
Warehouse-Native Experimentation helps you make informed product decisions using the data and models your business already trusts. Run experiments where your data lives, define metrics that reflect your reality, and ship features faster with confidence.