Real-time vs. warehouse-gated: Finding the right balance for your customer data infrastructure

We've noticed a trend advocating for warehouse-gated data architecture, whereby all data is always pushed to the warehouse before being sent to downstream tools. While we agree with many points about the value of centralizing customer data in your warehouse, we believe there's a critical nuance missing from this perspective: the power of real-time event streams.

Some thoughts about warehouse-gated architecture

A warehouse-gated approach can have some advantages of piping data to your warehouse, including:

• Event enrichment with comprehensive customer data
• Improved data governance and consistency
• Centralized identity resolution
• Simplified data management

These are valuable benefits that align with our own philosophy about the importance of warehouse-centric data infrastructure. But assuming all data should flow through the warehouse first–before reaching downstream tools–creates significant limitations for many use cases.

Critical limitations of a warehouse-gated approach

Latency issues for real-time activation

What's missing from the warehouse-gated approach is recognizing that not all data workflows benefit from sending data to your warehouse as the first mandatory step. In fact, for many customer engagement use cases, inserting the warehouse as a mandatory intermediary creates unnecessary latency that can negatively impact customer experiences.

Consider these common marketing scenarios:

• Abandoned cart recovery: When a customer leaves items in their cart, the effectiveness of recovery messaging decreases dramatically with each minute of delay
• Conversion optimization: Removing users from advertising audiences immediately after conversion prevents wasted ad spend and poor customer experiences
• In-session personalization: Tailoring a user's experience based on their current behavior requires millisecond-level response times

Even with data warehouses pushing toward real-time capabilities, the inherent process of landing data, computing results, and delivering outputs introduces unavoidable delays. While "near real-time" might be acceptable for some use cases, true real-time applications require a different approach.

The tag management reality

Another significant limitation of warehouse-gated approaches is the misconception around tag management. Using a single SDK to send all of your data to your warehouse cannot actually replace all of your integrations and tags. Many critical components still require their own presence on your website or application:

• Advertising pixels for retargeting and conversion tracking
• User interaction tools like push notifications
• Interactive elements like interstitials and chat widgets
• A/B testing and experimentation tools

Moving to a warehouse-gated approach often means adding another library to your site rather than streamlining collection around a central instrumentation layer. This can increase page weight and complexity rather than reducing it, negating one of the promised benefits of this architecture.

Cost implications

Routing every event through the warehouse via streaming or reverse ETL is significantly more expensive than sending events directly to destinations in real time. This cost difference becomes substantial at scale for several reasons:

• Double processing costs: Each event must be processed twice—once when it enters the warehouse and again when it exits via reverse ETL
• Warehouse compute expenses: Running frequent jobs to process and transform data in the warehouse incurs ongoing compute costs
• Storage overhead: Maintaining all events in the warehouse, even those only needed for immediate activation, increases storage costs
• Egress fees: Many cloud providers charge for data transferred out of their services, creating additional costs for moving data from the warehouse to destinations

For high-volume event streams, these costs can quickly add up, especially when many events don't really require warehouse processing to deliver their business value.

Complexity and increased failure modes

A warehouse-gated approach introduces multiple additional systems in the end-to-end pipeline, each with its own potential points of failure:

• Staging areas in cloud storage (like S3)
• Warehouse loading processes and tables
• Reverse ETL staging areas and synchronization processes

This complexity increases the risk of data flow disruptions. For example, warehouses often have strict data type limitations that can cause event loading failures. If events are being loaded into warehouse tables using a flattened architecture—where event properties are stored in separate columns—those loads can fail if a property's type is incompatible with the column type in the warehouse.

When a new property appears in an event or an existing property changes type, these mismatches can break the entire pipeline, requiring engineering intervention to fix schema issues before data can flow again. In contrast, direct event streaming typically handles schema evolution more gracefully.

The hybrid approach: combining warehouse power with event streaming speed

At RudderStack, we believe the optimal solution isn't choosing between warehouse-gated or real-time architectures—it's leveraging both in a complementary fashion.

Our approach recognizes that different use cases have different requirements:

For real-time use cases:

Direct event streaming to operational tools allows for immediate action based on customer behavior. Many use cases like enrichment, filtering, governance, user property reduction, CAPI, and consent can be handled through real-time event forwarding—without requiring warehouse processing.

For complex, computed insights:

The warehouse excels at aggregations, complex joins, and historical analysis. These computed traits (like lifetime value, purchase frequency patterns, or multi-touch attribution) benefit from warehouse processing and can be delivered via reverse ETL.

The key insight: The data volume requiring warehouse processing is orders of magnitude lower than the event volume requiring real-time delivery.

Real-world example: abandoned cart workflows

Let's compare approaches using a common e-commerce scenario:

Warehouse-gated approach:

1. User adds product to cart (event captured)
2. Event data lands in warehouse
3. Warehouse job processes abandonment criteria
4. Reverse ETL syncs audience to marketing tool
5. Marketing tool sends recovery message

This process introduces multiple points of latency (e.g., loading in and out of the warehouse, computing) in most implementations, during which the customer might have already gone elsewhere.

RudderStack's hybrid approach:

1. User adds product to cart (event captured)
2. Event streams directly to marketing automation platform in real-time
3. Marketing automation platform immediately triggers abandonment workflow based on real-time behavior
4. Simultaneously, event streams to warehouse for analytics and enrichment
5. Computed traits from warehouse (e.g., customer lifetime value) sync to marketing platform via reverse ETL to enhance targeting

The hybrid approach delivers the best of both worlds: immediate response to customer behavior, plus the rich context that only warehouse processing can provide.

Marketing automation platforms have evolved

Another consideration often overlooked is that modern marketing platforms like Braze and Iterable have sophisticated audience-building capabilities designed to work with real-time events. These platforms are specifically built to:

• Process event data in real-time
• Trigger messages based on behavioral signals
• Manage complex audience criteria
• Deliver omnichannel communications

By forcing all audience definition into the warehouse layer, you're effectively duplicating functionality and adding unnecessary latency to systems designed for real-time operation.

Finding the right balance for your business

Data infrastructure isn't one-size-fits-all. The optimal approach depends on your specific business needs, which could include:

• Event streaming first: When immediacy matters most
• Warehouse-gated: When complex computed traits drive decisions
• Hybrid approach: The ideal solution for most organizations

At RudderStack, we've built our platform to support all these approaches, with particular strength in real-time event streaming combined with warehouse integration.

Conclusion: Best of both worlds

The future of customer data infrastructure isn't about choosing between warehouse-gated or real-time architectures—it's about intelligently combining both approaches to meet your business needs.

While warehouse-gated data flows are important, we believe the most successful organizations will leverage both warehouse computation and real-time event streaming in complementary ways.

The next evolution of customer data infrastructure will be about orchestrating these dual pathways, using each for what it does best to create more responsive, personalized, and effective customer experiences.