Why Companies Like Netflix and Airbnb Use Amazon Redshift Data Marts (And You Should Too)

Why Companies Like Netflix and Airbnb Use Amazon Redshift Data Marts (And You Should Too)

Your Data Is Trapped, And It's Costing You Money

Picture this: You're the head of marketing at a growing e-commerce company. Black Friday is approaching, and you need to know which product categories performed best last year, which customer segments to target, and how much inventory to prepare. But getting these answers requires emailing three different departments, waiting for IT to run reports, and then trying to piece together data from various spreadsheets that somehow never quite match up.

Sound familiar? This scenario plays out thousands of times daily in companies worldwide. Your data exists—it's sitting in customer databases, sales systems, and marketing platforms—but accessing it quickly and reliably feels impossible. Meanwhile, companies like Netflix can instantly tell you which shows are trending in specific regions, and Airbnb knows exactly which neighborhoods are seeing booking surges in real-time.

The difference? They're using something called data marts, and increasingly, they're building them on Amazon Redshift.

What Exactly Is Amazon Redshift?

Think of Amazon Redshift as a massive, incredibly fast filing cabinet that lives in the cloud. But unlike a traditional filing cabinet where you have to search through folders one by one, Redshift can instantly search through millions of records and give you exactly what you need in seconds.

More technically, Redshift is Amazon's cloud-based data warehouse service. It's designed to store huge amounts of business data and run complex analyses lightning-fast. Companies use it to answer questions like "Which customers are most likely to cancel their subscriptions?" or "What's our profit margin by product line over the last two years?"

What makes Redshift special is that it can handle data that would crash Excel (we're talking billions of rows) while still delivering results faster than you can get a coffee. It's also "fully managed," meaning Amazon handles all the technical maintenance while you focus on getting insights from your data.

A Data Mart Is Your Company's Personal Analytics Goldmine

Now, here's where data marts come in. Imagine your company's data as a massive department store—thousands of products scattered across dozens of floors. Finding what you need takes forever, and you often leave empty-handed or with the wrong items.

A data mart is like creating a specialized boutique store focused on exactly what you need. Instead of wandering through the entire department store, you walk into a carefully curated space where everything is organized around your specific goals.

For example, Spotify's marketing team has a data mart focused entirely on user engagement—track skips, playlist additions, listening duration, and user demographics. They don't need to dig through Spotify's vast technical infrastructure data or billing information. Everything they need to understand user behavior and optimize marketing campaigns is right there, organized and ready to query.

Netflix takes this approach with content recommendations. Their recommendation data mart contains viewing history, user ratings, time spent watching, and content metadata. When they need to understand why certain shows perform better in different regions, they're not searching through Netflix's entire data ecosystem—they're working with a focused dataset designed exactly for content optimization.

How This Improves on What You're Probably Doing Now

Most companies today are stuck in what I call "data purgatory." They're either drowning in spreadsheets or waiting weeks for IT to generate reports. Here's how a Redshift data mart changes the game:

Instead of emailing IT for reports, your team gets self-service analytics. Airbnb's pricing team can instantly analyze how local events affect booking rates without submitting a ticket and waiting three days for results. They just run their queries directly against their pricing data mart.

Instead of reconciling data from multiple sources, everything is already integrated. Before Redshift, a typical analysis might require pulling data from Salesforce, Google Analytics, and your billing system, then spending hours making sure the numbers add up. With a data mart, all related data is already cleaned, integrated, and ready for analysis.

Instead of working with stale data, you get near real-time insights. Traditional reporting often works with data that's days or weeks old. Redshift data marts can be updated throughout the day, so when DoorDash wants to understand delivery patterns during lunch rush, they're seeing data from this afternoon, not last Tuesday.

Instead of analyses taking hours, they take minutes. What used to require overnight batch processing can now run in under a minute. This speed difference changes how teams work—they can ask follow-up questions, test hypotheses, and iterate on insights in real-time rather than planning analysis sessions days in advance.

What About Snowflake and Other Alternatives?

You might be wondering: "Is Redshift the only game in town?" Definitely not. The cloud data warehouse space has several strong players, with Snowflake being Redshift's biggest competitor.

Snowflake is often praised for being easier to set up and manage. It automatically handles many technical optimizations that require manual tuning in Redshift. Companies like Capital One and Adobe use Snowflake because it can scale up and down instantly based on demand, and you only pay for what you use down to the second.

Google BigQuery is Google's offering, which excels at analyzing truly massive datasets—think billions of rows. Companies like The New York Times use BigQuery to analyze reader behavior across their entire digital archive.

Microsoft Azure Synapse integrates tightly with other Microsoft tools, making it attractive for companies already using the Microsoft ecosystem.

So why choose Redshift? The main reasons are cost and ecosystem integration. If you're already using AWS services (which many companies are), Redshift integrates seamlessly with tools like S3 for data storage, Lambda for automation, and QuickSight for visualization. This integration can significantly reduce both complexity and costs.

Redshift also tends to be more cost-effective for consistent workloads, especially if you can commit to reserved capacity. Netflix, for instance, has predictable analytics needs that make Redshift's pricing model work well for them.

Real Companies, Real Results

Finra (the financial industry regulatory authority) uses Redshift to analyze 37 billion market events daily. Before Redshift, complex queries took hours or failed entirely. Now they can detect suspicious trading patterns in real-time, protecting investors and maintaining market integrity.

Lyft built a Redshift data mart specifically for driver analytics. They can instantly see which areas have driver shortages, how surge pricing affects driver participation, and which onboarding strategies work best in different cities. This data directly informs their expansion and pricing strategies.

McDonald's uses Redshift to optimize their supply chain and menu offerings. They can analyze which menu items perform best by location, time of day, and season, then adjust inventory and marketing accordingly. During the Travis Scott meal promotion, they could track performance in real-time and adjust supply distribution as needed.

The Bottom Line: Speed Wins

Here's what it really comes down to: in today's business environment, the companies that can answer questions faster make better decisions, and better decisions drive better results.

When Zoom saw explosive growth during the pandemic, they needed to quickly understand usage patterns, identify infrastructure bottlenecks, and optimize their service delivery. Their Redshift data marts allowed them to analyze user behavior and system performance in real-time, helping them scale smoothly during unprecedented demand.

That speed advantage compounds over time. Teams that can quickly test hypotheses, measure results, and iterate based on data will consistently outperform teams stuck waiting for monthly reports.

Getting Started: Think Small, Start Smart

If this sounds appealing but overwhelming, here's the secret: start with one specific business problem that's causing pain today. Don't try to solve everything at once.

Pick something like "understanding customer churn" or "optimizing marketing spend" or "improving inventory management." Build a focused data mart around that one use case, prove the value, and then expand from there.

The companies succeeding with Redshift didn't start by trying to revolutionize their entire data strategy overnight. They started by solving one important problem really well, then built from that success.

The question isn't whether your company will eventually need better data analytics—it's whether you'll be ahead of the curve or playing catch-up while competitors gain ground with faster, better decision-making.

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Aaron Rose is a software engineer and technology writer.