Untangling the Knot: The Challenge of Integrating eCommerce Data in Real-Time

In the dynamic realm of eCommerce, the ability to make quick, data-driven decisions is a key part of capturing shoppers' attention. However, the reality for many backend and operations developers is one of complexity and challenge as they navigate the labyrinth of connecting disparate data sources in real-time that enriches the shopping experience.

The landscape of real-time events and stream processing is teeming with myriad systems—Kafka, Apache Flink, and Google Pub/Sub, to name a few. Each platform has its merits, yet linking these data pipelines and ensuring seamless data flow is far from trivial. Developers have to grapple with the complexities of different APIs, wrangle with the nuances of diverse programming languages, and manage the intricacies of various cloud providers.

Knowledge graphs provide a powerful way to make sense of this data deluge. These semantic tools allow developers to map intricate relationships between different data points, providing a holistic view of the business. Connecting point-of-sale (POS) systems to these knowledge graphs typically involves API calls and webhooks. However, the real challenge lies in integrating these systems with other inventory and warehousing systems, often involving proprietary software and complex data mappings.

Omnichannel retail

When it comes to omnichannel operations—where goods are sold through multiple channels—connecting these systems to main databases and data lakes or warehouses adds another layer of complexity. ETL processes, batch jobs, and APIs are commonly used to pull data from these systems. Yet, the rise of IoT and the increasing volume of data generated necessitate a more robust approach.

The argument here isn't about the inadequacy of these systems but rather the inherent limitations of a "best of breed" approach. In a world of hundreds or thousands of events, picking the best tool for each job may have been the right strategy. But as we move into an era of global data, high-speed systems, IoT sensors, and heightened security concerns, this approach feels increasingly outdated.

Why? Because each integration, each connection between systems, represents a potential point of failure. They are managed by people—lots and lots of people. And while human error is part of the equation, the real issue is the sheer complexity and magnitude of the task. As the number of data sources grows, so does the effort required to maintain and manage these integrations.

Real-time inventory

One of the most common pain points developers face is the need to keep inventory data up-to-date. This seemingly simple task can consume significant resources, leaving "less critical" projects—like improving site experience or optimizing load times—on the back burner. Yet, these projects are often closely tied to customer experience and directly impact revenue, highlighting the need for a more efficient approach.

The solution isn't to abandon these systems outright - rip and replace is a nuclear option. Rather, it's about consolidation—reducing the number of systems, streamlining processes, and optimizing data flow. It's about recognizing that in an increasingly complex and fast-paced world, the old ways of doing things may no longer serve us.