Data warehousing: Storing experiment data

If you've ever tried to make sense of experiment data scattered across different tools and spreadsheets, you know the pain. One team's running A/B tests in their own system, another's got metrics in a different database, and somehow you're supposed to piece it all together for that Monday morning meeting.

That's where data warehousing comes in - not as some abstract concept, but as a practical solution to a very real problem. Let's talk about how to actually store and manage your experiment data in a way that doesn't make you want to pull your hair out.

Understanding data warehousing for experiment data

Think of a data warehouse as your experiment data's permanent home. Instead of having test results living in five different places, you centralize everything in one spot. This isn't just about organization - it's about making sure everyone on your team is looking at the same numbers.

The beauty of centralizing experiment data is that it creates a single source of truth. No more "well, my dashboard says X but yours says Y" conversations. When the product team asks about last quarter's feature test, you know exactly where to look. And when the CEO wants to know why conversion rates changed, you can pull up the data without digging through old emails.

Data warehouses are built to handle the scale that comes with serious experimentation. We're talking about storing millions of user interactions, test variations, and metric calculations. The folks at LinkedIn discovered this firsthand when they revamped their experimentation engine - proper data architecture made their system 20x faster.

But here's the thing: data warehousing enables the kind of analysis that actually moves the needle. You can run complex queries across months of experiments, spot patterns you'd never see in isolated tests, and make decisions based on real evidence. SQL databases give you the power to slice and dice your data however you need, whether that's comparing seasonal trends or understanding user segments.

Setting up your warehouse does require some planning though. You'll need to think about:

• How much data you're generating (spoiler: it's probably more than you think)

• Who needs access and what they'll do with it

• Security measures to keep sensitive user data protected

• Whether you need a dedicated development environment for testing

Best practices in storing experiment data in data warehouses

Let's get practical about modeling your experiment data. The way you structure your tables can make the difference between queries that run in seconds versus minutes. Start by thinking about your core entities: users, experiments, variants, and metrics. Each should have its own table with clear relationships.

Here's what works well:

• Store raw event data separately from aggregated metrics

• Use consistent naming conventions (trust me, future you will thank present you)

• Include metadata about when experiments ran and who owned them

• Keep a change log for experiment configurations

Your ETL pipeline is where the magic happens - or where everything falls apart. Build it to handle the unexpected because experiment data is messy. Users do weird things, tracking breaks, and sometimes your engineers forget to tell you about that new event they added. Automation helps, but you still need monitoring to catch when things go sideways.

Data quality isn't sexy, but it's essential. Nothing ruins credibility faster than presenting results based on bad data. Set up validation checks that run automatically: row counts that make sense, metric values within expected ranges, and alerts for when things look off. The team at Google discovered through their BigQuery proof-of-concept that modern warehouses can handle these quality checks at scale without breaking a sweat.

Speaking of modern solutions, platforms like BigQuery or Snowflake aren't just buzzwords - they're genuinely useful for experiment data. They handle the heavy lifting of distributed computing, so you can focus on analysis instead of infrastructure.

Overcoming challenges in experiment data storage

Volume and velocity - these are your two biggest headaches with experiment data. A single A/B test can generate millions of events per day, and you need to process them fast enough to make timely decisions. The data engineering community on Reddit regularly debates solutions, with many pointing out that a dedicated development environment is crucial for testing at scale before you hit production.

Schema changes will haunt you if you're not prepared. Your product team will want to track new things, experiment designs will evolve, and suddenly your carefully planned tables don't fit anymore. The solution? Build flexibility in from the start. Some teams swear by schemaless approaches, storing raw JSON and parsing it later. Others prefer strict schemas with versioning. Pick what works for your team's discipline level.

Security is non-negotiable when you're storing user behavior data. You need the right balance:

• Analysts should access what they need without jumping through hoops

• Personal information must stay protected

• Audit trails should track who looked at what

• Data retention policies need to be enforced automatically

This is where solutions like Statsig's Warehouse Native shine - they run computations directly in your warehouse, so sensitive data never leaves your controlled environment.

Leveraging modern data warehousing for experimentation

The game has changed with warehouse-native experimentation platforms. Instead of copying data between systems, you run experiments directly where your data lives. This approach eliminates synchronization headaches and lets you leverage your existing data for richer analysis.

Cloud warehouses have made scalability a non-issue. Need to analyze a year's worth of experiments? Just scale up for a few hours and scale back down. The economics finally make sense for teams that aren't Google-sized. You get:

• Massive parallel processing for complex queries

• Columnar storage that's perfect for analytical workloads

• Built-in connections to visualization and ML tools

• Pay-per-query pricing that won't blow your budget

AI and machine learning are starting to change how we think about experiment analysis. Instead of just calculating p-values, you can use ML to identify user segments that respond differently to tests, predict long-term impact from short-term results, and even suggest what to test next. The key is having your data organized and accessible - which brings us back to good warehousing practices.

Statsig's platform exemplifies this modern approach, offering feature flags and automated rollouts that integrate directly with your data warehouse. No more data silos, no more synchronization delays.

Closing thoughts

Data warehousing for experiments isn't just about storage - it's about building a foundation for better decision-making. When your experiment data is centralized, well-structured, and easily accessible, you can move from gut-feel decisions to data-driven ones. The challenges are real, but so are the solutions.

Start small if you need to. Pick one team's experiments, get them into a proper warehouse, and show the value. Once people see what's possible with organized data, you'll have allies in expanding the system.

Want to dive deeper? Check out Martin Fowler's writings on data lakes versus warehouses, explore the data engineering subreddit for practical tips, or take a look at how Statsig handles warehouse-native experimentation.

Hope you find this useful! Now go centralize those experiments.