What is a data warehouse and why is it important for experimentation?

If your product or marketing team wants to run a test based on churn risk and recent support activity, how long would it take for them to launch it?

No two organizations will have exactly the same answer, because it depends on the experimentation platform… and data warehouse. 

For leading companies, experimentation is more than A/B testing tools. It’s a fully connected stack of tools designed in a way that lets them move fast without losing data. 

In other words, it’s built with tools that are designed to integrate with each other. No manual exports. No custom pipelines.

Kameleoon’s two-way integration with Amazon Redshift allows users running both web and feature experiments to create highly personalized user experiences and send the data right back to Redshift quickly and easily.

In this article, we’ll break down exactly what this means for your tech stack, and why your data warehouse setup might matter more than you think.

What is a data warehouse?

A data warehouse (DWH) is a centralized system that stores and organizes large amounts of data. Data warehouses are designed for analysis and decision-making. 

Modern data warehouses, such as BigQuery, Snowflake, and Amazon Redshift, are “the” sources of truth for their teams. They contain customer data, generate reports, and allow for intelligent forecasting.

Data warehouses and experimentation

Data warehouses are filled with insights that any experimenter can use to improve conversions.

Unfortunately, a lot of experimentation platforms fail to integrate with popular data warehouses. When that happens, insights become stuck. Like siloed teams, the data can’t work in a cross-functional way.

Many platforms claim to support data warehouse integration, but only send the information one way; they push data to the data warehouse, but don’t pull, or read, data from the data warehouse. Others that can pull metrics and audience data do so only for reporting purposes. 

These platforms miss a crucial opportunity to improve their experiments by pulling data for targeting, making it difficult to target audiences using data stored in your data warehouse. The result is usually that developers need to manually extract and prepare the data—an often time-consuming and frustrating task.

With Kameleoon’s new Redshift integration, experimentation becomes a two-way street. You can:

• Pull user segments and metrics from Redshift into Kameleoon
• Push experiment exposure events into Redshift for centralized reporting
• Combine live data, such as clicks, adds to cart, or time spent on site, from your site or app with Redshift segments for real-time targeting and personalization

Experiment faster using Redshift data

Once you’ve connected Redshift to your project in Kameleoon, you can access the exact data you need to run stronger, data-backed, and more personalized experiments.

For example, you can use Amazon Redshift as a source to leverage its data by configuring Data Ingestion Tasks. These tasks allow you to regularly retrieve specific data from Redshift, based on predefined SQL queries and frequencies.

You can then use the data collected through these tasks as targeting conditions within your Kameleoon campaigns, enabling highly personalized and data-driven user experiences.

Kameleoon can also use Redshift as a destination, rather than a source. Kameleoon automatically sends experiment exposure events to Redshift, keeping all of your most important data in one familiar place.

One platform and one data source for every team

The most successful experimentation programs work together across teams. 

According to the 2025 Experimentation-led Growth Report, the fastest-growing companies are the ones that allow—or better, encourage!—their marketing, product, and engineering teams to test together, using shared data and aligned goals.

With data warehouse integrations, this goal is an easier reality. If your business uses Amazon Redshift as its data warehouse, this integration makes Kameleoon an even more powerful tool in your tech stack.

You don’t need to change how your data is structured or stored. You just need to connect, configure, and start testing. 

Ready to connect Redshift to your experimentation program? Reach out to your Customer Success Manager to enable the integration.