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Delays are a significant challenge for the airline industry. They disrupt travel plans, erode customer loyalty, and inflict significant financial losses. In an industry built on precision and punctuality, even minor setbacks can have cascading effects. Whether due to adverse weather conditions or unforeseen technical issues, these delays ripple through flight schedules, affecting both passengers and operations managers. While neither group is typically at fault, the ability to quickly reallocate resources and return to normal operations is crucial.

To mitigate these disruptions and restore passenger trust, airlines must have the tools and strategies to quickly identify delays and efficiently reallocate resources. This blog explores how a unified platform with real-time data analysis can be a game-changer in this regard especially in saving costs.

The high cost of delays

Delays from disruptions, like weather events or crew unavailability, pose major challenges for the airline industry. These delays have significant financial impact according to some studies, costing European airlines on average €4,320 per hour per flight. They also create operational challenges like crew disruptions and reduced airplane availability, leading to further delays, which is known in the industry as delay propagation.

To address these challenges, airlines have traditionally focused on optimizing their pre-flight planning processes. However, while planning is crucial, effective recovery strategies are equally essential for minimizing the impact of disruptions. Unfortunately, many airlines have underinvested in recovery systems, leaving them ill-prepared to respond to unexpected events. The consequences of this imbalance include:

• Delay propagation: Initial delays can cascade, causing widespread schedule disruptions.

• Financial and operational damage: Increased costs and inefficiencies strain airline resources.

• Customer dissatisfaction: Poor disruption management leads to negative passenger experiences.

The power of real-time data analysis

In response to the significant challenges posed by flight delays, a real-time unified platform offers a powerful solution designed to enhance how airlines manage disruptions.

Event-driven architectural approach

The diagram below showcases an event-driven architecture that can be used to build a robust and decoupled platform that supports real-time data flow between microservices. In an event-driven architecture, services or components communicate by producing and consuming events, which is why this architecture relies on Pub/Sub (messaging middleware) to manage data flows.

Moreover, MongoDB’s flexible document model and ability to handle high volumes of data make it ideal for event-driven systems. Combining these features with PubSub’s, this approach proves to offer a powerful solution for modern applications that require scalability, flexibility, and real-time processing.

Figure 1: Application architecture

In this architecture, the blue line in the diagram shows the operational data flow. The data simulation is triggered by the application’s front-end and is initialized in the FastAPI microservice. The microservice, in turn, starts publishing airplane sensor data to the custom Pub/Sub topics, which forwards these data to the rest of the architecture components, such as cloud functions, for data transformation and processing.

The data is processed in each microservice, including the creation of analytical data, as shown by the green lines in the diagram. Afterward, data is introduced in MongoDB and fetched from the application to provide the user with organized, up-to-date information regarding each flight.

This leads to more precise and detailed analysis of real-time data for flight operations managers. As a result, new and improved opportunities for resource reallocation can be explored, helping to minimize delays and reduce associated costs for the company.

Microservice overview

As mentioned earlier, the primary goal is to create an event-driven, decoupled architecture founded on MongoDB and Google Cloud services integrations. The following components contribute to this:

• FastAPI: Serves as the main data source, generating data for analytical insights, predictions, and simulation.

• Telemetry data: Pulls and transforms operational data published in the PubSub topic in real-time, storing it in a MongoDB time series collection for aggregation and optimization.

• Application data: Subscribed to a different PubSub topic, this service acknowledges static operational data, including initial route, recalculated route, and disruption status. Therefore, this service will only be triggered provided any of the previous fields are altered. Finally, this data is updated in its MongoDB collection accordingly.

• Vertex AI integration—analytical data flow: A cloud function triggered by PubSub messages that executes data transformations and forwards data to the Vertex AI deployed machine learning (ML) model. Predictions are then stored in MongoDB.

MongoDB: A flexible, scalable, and real-time data solution

Building a unified real-time platform for the airline industry requires efficient management of massive, diverse datasets. From aircraft sensor data to flight cost calculations, data processing and management are central to operations. To meet these demands, the platform needs a flexible data platform capable of handling multiple data types and integrating with various systems. This enables airlines to extract valuable insights from their data and develop features that improve operations and the passenger experience.

Real-time data processing is a must-have feature. This allows airlines to receive immediate alerts about delays, minimizing disruptions and ensuring smooth operations. In fast-paced airport environments, where every minute counts, real-time data processing is indispensable.

For example, integrating MongoDB with Google Cloud’s Vertex AI allows for the real-time processing and storage of airplane sensor data, transforming it into actionable insights.

Business benefits

This solution provides real-time access to critical flight data, enabling efficient cost management and operational planning. Immediate access to this information allows flight operation managers to plan ahead, reallocate existing resources, or even initiate recovery procedures in order to diminish the consequences of the identified delay.

Moreover, its ML model customization ensures adaptability to various use cases.

Regarding the platform’s long-term sustainability, it has been purposely designed to integrate highly reliable and scalable products in order to excel in three key standards:

Scalability

• The platform’s compatibility with both horizontal and vertical scaling is clearly demonstrated by its integral design.

• The decoupled architecture illustrates how this solution is divided into different components—and therefore instances—that work together as a cohesive whole.

• Vertical scalability can be achieved by simply increasing the computing power allocated to the designed Vertex AI model, if needed.

Availability

• The decoupled architecture exemplifies the central importance of availability in any project’s design.

• Using different tracks to introduce operational and analytical data into the database allows us to handle any issues in a way that remains unnoticeable to end users.

Latency

• Optimizing the connections between components and integrations within the product is key to achieving the desired results.

• Using PubSub as our asynchronous messaging service helps minimize unnecessary delays and avoid holding resources needlessly.

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To sum up, this blog has explored how MongoDB can be integrated into an airline flight management system, offering significant benefits in terms of cost savings and enhanced customer experience.