Building Modern Omnichannel Ordering on MongoDB

Solution Overview

Customers today expect seamless shopping experiences that use online and offline channels, such as buying online for pickup or delivery. However, retailers struggle to implement technical solutions that support omnichannel shopping experiences. Retailers need a flexible system that provides real-time visibility, handles dynamic traffic patterns, and enables modern integrations across all customer touchpoints.

This solution demonstrates how to build an omnichannel e-commerce website using MongoDB Atlas. The demo lets users choose their preferred shipping method (BOPIS or home delivery) and track their order progress in real time until delivery. By leveraging key MongoDB features including Change Streams, Atlas Triggers, and the flexible document model, you'll learn how to create a robust, scalable omnichannel ordering system.

Reference Architectures

This solution uses MongoDB Change Streams and Atlas Triggers to enable real-time data operations across multiple applications. The architecture consists of two main components: Real-Time Apps and MongoDB Atlas.

Real-Time Apps include any application that responds to database changes, such as e-commerce, distribution center, warehouse, and inventory apps.

This solution has the following workflow:

1. An action from an application produces a data modification, such as an order status update.

2. The event is registered in the orders collection in MongoDB Atlas.

3. These changes are recorded in the oplog, which uses the oplog.rs collection.

4. The Change Streams API monitors these changes. You can monitor changes at the collection, database, or cluster-wide levels and filter specific changes using the MongoDB Aggregation Framework.

5. Based on your architecture needs, you can:

   • Perform an update on the application side.

   • Trigger an automated task through Atlas Functions.

This architecture provides a foundation for various real-world applications, such as:

• Real-time notifications: Automatic customer alerts for order pickup readiness.

• Reactive systems: Smart inventory management with automatic restocking.

• Event-driven architecture: Real-time microservices synchronization with optional Kafka integration.

• Change data capture: Immediate system-wide product price updates.

• Real-time analytics: Instant fraud risk recalculation using ML models.

Figure 1. Omnichannel ordering solution architecture

Data Model Approach

This solution's data model uses two primary collections, named products and orders. Each collection uses a field: value format to provide an organized representation of the stored data.

Products Collection

The products collection stores comprehensive product information such as the following:

• ID, name, and a unique product identifier (such as SKUs).

• Categorization fields, such as masterCategory, subCategory, and articleType.

• Nested objects for pricing with amount and currency fields.

• Product-specific attributes such as autoreplenishment status and base color.

• Inventory-related fields and product metadata.

Figure 2. Example document in products collection

Orders Collection

The orders collection tracks order details, including the following:

• Unique identifiers for orders and users.

• An array of products for each order.

• status_history array, which captures the progression of order states with timestamps.

• Differentiation between order types (Buy Online, Pick up in Store vs Buy Online, Get Delivery at Home).

• Order-specific shipping addresses.

• Status workflows based on delivery type:

  • BOPIS orders show statuses, such as "In process" or "Ready for pickup."

  • Home delivery orders progress through the following states: "In process," "Ready for delivered," "Picked up from warehouse," "In Transit," and "Delivered."

Figure 3. Example documents in the orders collection

Build the Solution

This solution uses this GitHub repository. The following procedure outlines how to set up the demo. For a complete guideline and implementation details, see the repository's README.

Key Learnings

• Flexibility and speed: MongoDB's flexibility allows you to adapt your data structures with ease, leading to faster time to market and a consistent omnichannel experience.

• Real-time capabilities: Features like Change Streams and Atlas Triggers enable real-time data processing, which is essential for tasks like order tracking and inventory updates.

• Smart architecture: MongoDB's scalable and flexible architecture makes it simple to integrate tools such as Change Streams and Atlas Triggers without extra application layers.

Author

• Angie Guemes, MongoDB

• Florencia Arin, MongoDB

Learn More

• Event-Driven Inventory Management System

• Real-Time Personalization with Receipt Data

• RFID: Real-Time Product Tracking