The scale of modern recommendation systems is staggering. It’s billions of user interaction events per day and petabytes of historical data. Each of these data points contributes to understanding user preferences and missing even a small percentage can translate to significant revenue impact. In this article, I want to share the lessons from building these systems, particularly the decisions that seem small at first but compound into major challenges later.
Designing the Data Pipeline
The foundation of any recommendation system is understanding what data to collect and how to move it through your infrastructure. At the most basic level, you’re capturing events: user views, clicks, add-to-carts, purchases and countless other interactions. But the real value comes from enriching these events with context. All of this metadata (time, device, OS, session history) becomes crucial when training models and debugging issues.
One of the earliest architecture decisions you’ll face is choosing between streaming and batch processing. Streaming architectures using tools like Kafka or Kinesis allow you to capture events in real-time and make them available for immediate processing. This is important if you want your recommendations to reflect recent user behavior. Batch processing, on the other hand, is simpler to implement and can be more cost-effective for certain workloads. In practice, most mature systems end up with a hybrid approach.
The question of data lake versus data warehouse is also an important one. A data lake gives you flexibility to dump raw, unstructured data cheaply, whereas a warehouse gives you structure and fast query capability. My experience is that you will want both. The data lake is your source of truth that contains all raw events exactly as they were captured. The warehouse provides cleaned, structured views that are optimized for specific use cases like analytics and feature engineering.
Another thing to note is schema design. It seems simple to create a highly flexible schema that will fit into any potential future needs, but that will be a nightmare of optional fields and unclear contracts between systems. Spend the time up front to declare clear event schemas with required fields and strong typing. Version your schemas from day one and build tooling to handle migrations elegantly.
Partitioning strategy deserves special attention. How you partition your data affects everything from query performance to cost to operational complexity. Time-based partitioning is a natural choice for event data, but you might also want to partition by user cohort, geography, or product category depending on your access patterns.
Ensuring Scalability and Reliability
Capacity planning is not optional. In the initial stages of development, you typically have enough resources and the primary goal is to get something working. But as your system gains traction and more teams start relying on it, resource constraints become a major bottleneck. By the time you’re in production serving critical use cases, balancing resources becomes extremely difficult. The solution here is to plan ahead and understand your domain well enough to predict data volumes at least a year out.
Horizontal scaling is your friend. Design your ingestion pipeline so you can add more shards, more workers and more storage without architectural changes. Implement autoscaling based on queue depth or CPU utilization. Use load balancing to distribute traffic evenly. These patterns are well-established, but they require upfront investment in architecture and tooling.
The most important function of a data collection system is maintaining high data quality. This cannot be overstated. In large-scale systems, even a small drop in data logging can lead to huge revenue losses. I learned this the hard way when we missed a significant data loss for one device type because we were only monitoring overall volume. The total data volume looked stable because it was so large, but we had completely stopped collecting data from a specific device type. Users on those devices were essentially invisible to our recommendation system.
Handling failures gracefully is another critical requirement. Events will get lost, APIs will time out, dependencies will fail. Your system needs to handle these scenarios without cascading failures. Implement retry logic with exponential backoff. Use dead letter queues to capture events that can’t be processed. Build circuit breakers to prevent overloading downstream systems. And crucially, make everything observable so you can quickly diagnose issues when they occur.
Balancing cost, performance and operational complexity is an ongoing challenge. The most robust solution is often expensive. The cheapest solution is often brittle. You need to find the sweet spot for your organization and that sweet spot changes as you scale. Be prepared to re-evaluate your architecture periodically and make hard choices about where to invest in reliability versus where to accept some risk.
Closing the Feedback Loop
A recommendation system is only as good as its ability to learn from its own predictions. This requires closing the feedback loop: capturing not just what was shown to users but what they did in response, then feeding that information back into model training.
Labeling pipelines is usually a bottleneck. You need to join interaction events with the recommendations that were shown, determine which actions constitute positive signals, handle delayed conversions and deal with missing data. This is harder than it sounds because events arrive out of order, sessions span multiple devices and the definition of a “conversion” might be nuanced. Invest in robust joining logic and prepare to handle edge cases.
Feature store integration is increasingly important as recommendation systems mature. A feature store provides a centralized repository for feature definitions and computed features, enabling consistent feature engineering across training and serving. But integrating with a feature store requires careful attention to data freshness. Your collected data needs to flow into the feature store quickly enough that models can use fresh signals, which might mean building streaming pipelines alongside your batch processes.
Experimentation support is another area where many data collection systems fall short. When your data collection system is in production, making changes to existing logging logic becomes risky. To understand the impact of new logic, you need to compare end-to-end model performance between the old and new approaches. This is difficult without an experimentation framework built into the system from the start.
Conclusion
Building large-scale data collection infrastructure for recommendation systems is a journey of continuous evolution. Your data needs will grow, your product requirements will change and new opportunities will emerge that you couldn’t anticipate. The key is building systems that are adaptable without being over-engineered.
Most importantly, remember that these systems exist to serve users. Every architecture decision should ultimately tie back to improving recommendation quality, which means better experiences for the people using your product. The technical challenges are significant, but they’re in service of a goal that makes the effort worthwhile.
