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Digital Retail Leader Article's Rapid Monolith Breakup with Apache Kafka
Technology Category
- Analytics & Modeling - Real Time Analytics
- Application Infrastructure & Middleware - Event-Driven Application
Applicable Industries
- Consumer Goods
- Retail
Applicable Functions
- Procurement
- Warehouse & Inventory Management
Use Cases
- Last Mile Delivery
- Time Sensitive Networking
Services
- Training
The Challenge
Article, a digital retail leader, was faced with a sudden surge in demand during the COVID-19 pandemic. The company, which had already embarked on a 2-5 year project in 2019 to modernize their systems by breaking up their monolith into domain applications, was forced to accelerate this process. The pandemic-induced digital shift led to a significant increase in the eCommerce share of the furniture market. Article, being a digital-only retailer, was faced with a tsunami of orders, equivalent to three months’ worth of Black Fridays. The engineering team had to prioritize protecting customer experience, optimizing order processing, tracking, and fulfillment, and improving communication services to keep customers updated on their orders. The developers also had to navigate complex questions about Apache Kafka, a technology they were using as part of their event-driven architecture.
About The Customer
Article is a digital retail leader focused on making it easy for people to furnish their spaces. They build software not only for themselves but also for their suppliers, orchestrating the entire supply chain from customer order to final-mile delivery. The company is unique in its approach, and its engineering team is committed to modernizing their systems and improving customer experience. During the COVID-19 pandemic, Article experienced a significant increase in demand, which drove them to accelerate their development and optimize their order processing and fulfillment systems.
The Solution
Article's engineering team, led by Laurent, used Lenses to operate Apache Kafka without needing a deep understanding of the technology. This allowed them to focus on developing more than 13 microservices. Lenses provided a way to visualize and operate their streaming data landscape. It made navigating Kafka easier by using a real-time data catalog & SQL instead of command line guesswork to troubleshoot their applications. The team could immediately see consumers, view the lag, and check whether data was moving through their applications. They could also view and evolve schemas from a UI with full security and auditing, without needing to understand a new set of APIs. Additionally, engineers could use Lenses to view, alert, and deploy CDC or other Kafka Connect connectors.
Operational Impact
Quantitative Benefit
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