Overcoming Fragmentation in Multi-Modal Transportation Data
Our client is a multinational information technology company providing comprehensive IT and telecommunications services to the air transport industry. Serving airlines, airports, ground handlers, and governments worldwide, they deliver solutions for passenger processing, baggage handling, aircraft operations, and more.
Modern transportation ecosystems extend beyond individual systems, with passenger journeys spanning multiple modes such as flights, trains, and maritime services. As transportation ecosystems expanded across multiple modes, the lack of integration and coordination created significant operational challenges.
- Fragmented data across transportation modes: Aviation, rail, and maritime systems operated in silos, limiting visibility into connected journeys and external disruptions.
- Reactive instead of proactive disruption management: Operators responded only after disruptions occurred, with no early warning or cross-modal coordination to prevent cascading impacts.
- No foundation for scalable intelligence: Inconsistent data standards and disconnected systems prevented unified analytics, real-time insights, and future AI adoption.
A new architectural approach was required to unify the fragmented data sources, enable real-time intelligence, and support future AI-driven capabilities.
Engineering a Scalable Intermodal Data Intelligence Platform
Addressing these challenges required a platform capable of integrating diverse data sources, processing information in real time, and delivering actionable insights across transportation systems.
Our team engaged as a strategic engineering partner to design a flexible, scalable architecture built on Databricks.
1. Implementing a Data Mesh Architecture for Multi-Source Integration
A data mesh architecture was introduced to ingest and manage data from over ten disparate sources, including aviation systems, rail feeds, maritime tracking, and external APIs. Each data domain retained autonomy while enabling cross-domain analytics.
A federated approach allowed the platform to integrate diverse data formats and update frequencies without requiring rigid standardization, enabling faster onboarding of new data sources and improved scalability.
2. Designing a Medallion Architecture
A layered medallion architecture was implemented to structure data processing across Bronze, Silver, and Gold layers. Raw data is captured and preserved in the Bronze layer, while the Silver layer standardizes and cleanses data, applying historization techniques to track changes over time.
The Gold layer delivers business-ready datasets and intelligence outputs, ensuring high data quality, traceability, and flexibility for both real-time and historical analysis.
3. Enabling Real-Time Intelligence and Disruption Detection
Specialized intelligence layers were developed to monitor disruptions across transportation modes in near real time. Detection engines identify delays, cancellations, and diversions, enabling operators to anticipate risks and take preventive action.
Real-time alerting allows teams to respond proactively, reducing the impact of disruptions and improving coordination across connected journeys.
4. Building API-Ready Data Access for Operational Use
Automated pipelines were implemented to synchronize processed data with operational databases and expose it through APIs. Separation of analytical and operational workloads ensures that real-time applications can access reliable data without impacting performance.
API-driven access enables seamless integration with external systems and supports real-time decision-making across transportation operators.
5. Ensuring Scalability for Multi-City Expansion
The platform was designed for both horizontal and vertical scalability, allowing expansion to new cities and additional data sources while supporting increasingly advanced AI use cases.
Standardized data contracts and modular ingestion patterns enable rapid deployment across new environments, while the architecture supports future predictive analytics, machine learning, and intelligent automation capabilities.
“Integrating aviation, maritime, and rail data is architecturally challenging because each mode uses different standards, update cycles, and even different definitions of basic concepts like ‘delay.’ On Databricks, we addressed this with Unity Catalog for clean environment separation, reusable connector repositories, and a layered intelligence model we call the ‘Brain Layer.’ This allowed us to move beyond isolated, per-mode alerts and build true intermodal intelligence that analyzes entire journeys and detects cascading risks. The platform is designed to handle constant schema changes and inconsistent feeds, creating infrastructure that continuously learns as new data sources come online.”
Vadym Mariiechko
Data Engineer – Addepto
Transforming Transportation Data into Connected Travel Intelligence
The partnership helped the client transform from fragmented transportation systems into an AI-based intermodal data platform that enables full visibility across connected journeys.
Operators can now monitor passenger flows in real time, identify risks earlier, and coordinate responses across multiple transportation modes.
Before
- Siloed data across aviation, rail, and maritime systems
- No visibility into connected journeys or connection risks
- Reactive disruption management
- Manual analysis across disconnected systems
- Limited to single-mode insights
- No awareness of external disruptions (weather, strikes, etc.)
- Platform limited to one location with no scalability
After
- Unified data mesh platform across all transportation modes
- Real-time visibility into multi-modal journeys and risks
- Proactive disruption detection and response
- Automated intelligence with near real-time alerts
- Cross-modal coordination across transport operators
- Integrated external data for full situational awareness
- Scalable architecture for expansion across cities
A scalable, data-driven architecture positions the platform to expand across additional cities and support advanced AI capabilities, including predictive analytics and automated decision support.
As part of KMS Technology, Addepto continues to deliver enterprise-grade data and AI solutions that help organizations transform complex transportation ecosystems into intelligent, connected networks.
Ready to build real-time decision intelligence? Contact us today!