Kai WaehnerThe digitalization of airports faces challenges such as integrating diverse legacy systems, ensuring cybersecurity, and managing the vast amounts of data generated in real-time. The vision for a digitalized airport includes seamless passenger experiences, optimized operations, consistent integration with airlines and retail stores, and enhanced security through the use of advanced technologies like IoT, AI, and real-time data analytics. This blog post shows the relevance of data streaming with Apache Kafka and Flink in the aviation industry to enable data-driven business process automation and innovation while modernizing the IT infrastructure with cloud-native hybrid cloud architecture. Schiphol Group operating Amsterdam Airport shows a few real-world deployments.
The Digitalization of Airports and the Aviation Industry
Digitalization transforms airport operations and improves the experience of employees and passengers. It affects various aspects of airport operations, passenger experiences, and overall efficiency.
Schiphol Group is a Dutch company that owns and operates airports in the Netherlands. The company is primarily known for operating Amsterdam Airport Schiphol, which is one of the busiest and most important airports in Europe. The Schiphol Group is involved in a range of activities related to airport management, including aviation and non-aviation services.
Schiphol Group describes its journey of becoming a leading autonomous airport until 2050:
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Data Streaming with Kafka and Flink for Airport Operations and Passenger Experience
Data streaming with Apache Kafka and Apache Flink enables airport and aviation systems to process and analyze real-time data from various sources, such as flight information, passenger movements, and baggage tracking, enhancing operational efficiency and passenger experience.
These technologies facilitate predictive maintenance, personalized services, and improved security measures through the continuous flow and immediate processing of critical data at any scale reliably.
Continuous Stream Processing in an Event-driven Architecture with Kafka Streams, KSQL, or Flink
Continuous processing of incoming events in real-time enables transparency and context-specific decision making. OpenCore, an IT consultancy in Germany, presented already in 2018 at Kafka Summit San Francisco how stream processing with technologies like Kafka Streams, KSQL or Apache Flink serves the real-time needs of an airport.
Think about the technical IoT events ingested from aircraft, gates, retail stores, passenger mobile apps, and many other interfaces…
… and how continuous correlation of data in real-time enables use cases such as predictive forecasting, planning, maintenance, plus scenarios like cross-organization loyalty platforms, advertisement, and recommendation engines for improving the customer experience and increasing revenue:
Airport Digitalization with Data Streaming using Apache Kafka and Flink
Real-time data beats slow data. That’s true for almost any use in the aviation industry, including airports, airlines, and other involved organizations. Additionally, data consistency matters across organizations.
Here are key areas where digitalization affects airports. While compiling this list, I realized I wrote about many of these scenarios in the past because other industry already deployed these use cases. Hence, each section includes a reference to another article where data streaming with Kafka and Flink is already applied in this context.
1. Passenger Experience
As frequent traveller myself, I put this at the beginning of the list. Examples:
- Self-service Kiosks: Check-in, baggage drop, and boarding processes have become faster and more efficient.
- Mobile Applications: Passengers can book tickets, receive real-time flight updates, and access boarding passes.
- Biometric Systems: Facial recognition and fingerprint scanning expedite security checks and boarding.
The past decade already significantly improved the passenger experience. But it still needs to get better. And data consistency matters. Today, a flight delay or cancellation is not shared consistently across the customer mobile app, airport screens, and customer service of the airline and airport.
Reference to data streaming in financial services: Operational and transactional systems leverage Kafka for data consistency, not because of its real-time capabilities. Apache Kafka ensures data consistency with its durable commit log, timestamps, and guaranteed ordering. Kafka connects to real-time and non-real-time systems (files, batch, HTTP/REST APIs).
2. Operational Efficiency
Automation with IoT sensors, paperless processes, and software innovation enables more cost-efficient and reliable airport operations. Examples:
- Automated Baggage Handling: RFID tags and automated systems track and manage luggage, reducing errors and lost baggage).
- Predictive Maintenance: IoT sensors and data analytics predict equipment failures before they occur, ensuring smoother operations.
- Air Traffic Management: Advanced software systems enhance the coordination and efficiency of air traffic control.
Reference to data streaming in manufacturing: Condition monitoring and predictive maintenance leverage stream processing with Apache Kafka and Flink for many years already, either in the cloud or at the edge and shop floor level for Industrial IoT (IIoT) use cases.
3. Security, Safety and Health Enhancements
Safety and health are one of the most important aspects at any airport. Airports continuously improved security, monitoring, and surveillance because of terrorist attacks, the Covid pandemic, and many other dangerous scenarios.
- Advanced Screening Technologies: AI-powered systems and improved scanning technologies detect threats more effectively.
- Cybersecurity: Protecting sensitive data and systems from cyber threats is crucial, requiring robust digital security measures.
- Health Monitoring: Temperature measurements and people tracking were introduced during the Covid pandemic in many airports.
Reference to data streaming in Real Estate Management: Apache Kafka and Flink improve real estate maintenance and operations, optimize space usage, provide better employee experience, and better defense against cyber attacks. Check out “IoT Analytics with Kafka and Flink for Real Estate and Smart Building” and “Apache Kafka as Backbone for Cybersecurity” for more details.
4. Sustainability and Energy Management
Sustainability and energy management in airports involve optimizing energy use and reducing environmental impact through efficient resource management and implementing eco-friendly technologies. Examples:
- Smart Lighting and HVAC Systems: Automated systems reduce energy consumption and enhance sustainability.
- Data Analytics: Monitoring and optimizing resource usage helps reduce the carbon footprint of airports.
Sustainability and energy management in an airport can be significantly enhanced by using Apache Kafka and Apache Flink to stream and analyze real-time data from smart meters and HVAC systems, optimizing energy consumption and reducing environmental impact.
Reference to data streaming in Environmental, Social, and Governance (ESG) across industries: Kafka and Flink’s real-time data processing capabilities build a powerful alliance with ESG principles. Beyond just buzzwords, I wrote about real-world deployments with Kafka and Flink and architectures across industries to show the value of data streaming for better ESG ratings.
5. Customer Service and Communication
Customer service is crucial for each airport. While lots of information comes from airlines (like delays, cancellations, seat changes, etc.), the airport provides the critical communication backend with display, lounges, service personal, and so on. Examples to improve the customer experience:
- AI Chatbots: Provide 24/7 customer support for inquiries and assistance with Generative AI (GenAI) embedded into the existing business processes.
- Digital Signage: Real-time updates on flight information, gate changes, and other announcements improve communication.
- Loyalty Integration: Airports do not provide a loyalty platform, but they integrate more and more with airlines (e.g., to reward miles for shopping).
Reference to data streaming in retail: The retail industry is years ahead with providing a hyper-personalized customer experience. “Omnichannel Retail and Customer 360 in Real Time with Apache Kafka” and “Customer Loyalty and Rewards Platform with Data Streaming” tell you more. GenAI is a fundamental change for customer services. Kafka and Flink play a critical role for GenAI to provide contextual, up-to-date information from transactional systems into the large language model (LLM).
6. Revenue Management
Airport revenue management involves optimizing income from aviation and non-aviation sources through demand forecasting and strategic resource allocation. Examples:
- Dynamic Pricing: Algorithms adjust prices for parking, retail spaces, and other services based on demand and other factors.
- Personalized Marketing: Data analytics help target passengers with tailored offers and promotions.
Reference to data streaming in retail: While the inventory looks different for an airport, the principles from retail can be adopted one-to-one. Instead of TVs or clothes, the inventory is the parking lot, lounge seat, and similar. Advertising is another great example. Airports can learn from many digital natives how they built a real-time digital ads platform with Kafka and Flink. This can be adopted to retail media in the airport, but also to any physical inventory management.
7. Emergency Response and Safety
Emergency response and safety at the airport involve coordinating real-time monitoring, quick decision-making, and efficient resource deployment to ensure the safety and security of passengers, staff, and infrastructure during emergencies. Examples:
- Real-time Monitoring: IoT devices and sensors provide live data on airport conditions, aiding in faster response times.
- Digital Simulation and Training: Virtual reality and simulation technologies enhance training for emergency scenarios.
- Seamless Connectivity: Stable Wi-Fi and 5G Networks with good latency and network slicing for safety-critical use cases.
Reference to data streaming in Industrial IoT: Safety-critical applications require hard real-time. This is NOT Kafka, Flink, or any similar IT technology. Instead, this is embedded systems, robotics, and programming languages like C or Rust. However, data streaming integrates the OT/IT world for near real-time data correlation and analytics in edge or hybrid cloud architectures. Every relevant data set from aircraft, gates, and other equipment is continuously monitored to ensure a safe airport environment.
Data Sharing with Kafka between Airport, Airlines and other B2B Partners like Retail Stores
Cross-organization data sharing is crucial for any airport and airline. Today, most integrations are implemented with APIs (usually HTTP/REST) or still even file-based systems. This works well for some use cases. But data streaming – by nature – is perfect for sharing streaming data like transactions, sensor data, location-based services, etc. in real-time between organizations:
As Apache Kafka is the de facto standard for data streaming, many companies directly replicate data to partners using the Kafka protocol. AsyncAPI as an open standard (beyond Kafka) and integration via HTTP on top of Kafka (via Kafka Connect API connectors) are other common patterns.
Real-World Success Stories for Data Streaming in the Aviation Industry
Several real world success stories exist for deployments of data streaming with Apache Kafka and Flink in airports and airlines. Let’s explore a few case studies and refer to further material.
Schiphol Group (Amsterdam Airport)
Roel Donker and Christiaan Hoogendoorn from Schiphol Group presented at the Data in Motion Tour 2024 in Utrecht, Netherlands. This was an excellent presentation with various data streaming use cases across fields like application integration, data analytics, internet of things, and artificial intelligence.
On its journey to an autonomous airport until 2025, the digitalization involves many technologies and software/cloud services. Schiphol Group transitioned from open source Apache Kafka to Confluent Cloud for cost-efficiency, elasticity, and multi-tenancy.
The company runs operational and analytical data streaming workloads with different SLAs. The integration team uses the data streaming platform to integrate with both the legacy and the new world, also 3rd party like airlines, GDS, police, etc (all point-to-point and with different interfaces).
Here are a few examples of the scenarios Schiphol Group explored:
Schiphol Group: Data Platform with Apache Kafka
Schiphol uses Apache Kafka as a core integration platform. The various use cases require different Kafka clusters depending on the uptime SLA, scalability, security, and latency requirements. Confluent Cloud fully manages the data streaming platform, including connectors to various data sources and sinks:
Kafka connects critical PostgreSQL databases, Databricks analytics platform, applications running in containers on Red Hat OpenShift, and others.
3Scale is used as complementary API gateway for request-response communication. The latter is not a surprise, but very common. HTTP/REST APIs and Apache Kafka complement each other. API Management solutions such as 3Scale, MuleSoft, Apigee or Kong connect to Kafka via HTTP or other interfaces.
Schiphol Group: IoT with Apache Kafka
Some use cases at Schiphol Group require connectivity and processing of IoT data. That’s not really a big surprise in the aviation industry, where airports and airlines rely on data-driven business processes:
Kafka Connect and stream processing connect and combine IoT data and feed relevant context into other IT applications.
Connectivity covers various infrastructures and networks, including:
- Private LoRa networks
- Passenger flow management system(FMS)
- BLIP (the supplier delivering IoT devices in the terminal measuring real-time how crowded areas are so people can be redirected when needed)
- Wi-Fi location services (like heatmaps for crowd management)
Schiphol Group: AI and Machine Learning with Apache Kafka
Artificial Intelligence (AI) requires various technologies and concepts to add business value. Predictive analytics, active learning, batch model training, debugging and testing the entire pipeline, and many other challenges need to be solved. Apache Kafka is the data fabric of many AI/ML infrastructures.
Here is how Kafka provides the foundation of an event-driven AI architecture at Schiphol Group:
The combination of Apache Kafka and AI/ML technologies enables various valuable use cases at Schiphol Group, including:
- Analysis of historical data (root cause analysis, critical path & process analysis, reporting)
- Insights on real-time data (insight on turnaround process with one shared truth, real time insight on ramp capacity and turnaround progress per ramp, real-time insight on ramp safety, input for E2E insight Airside
- Predictions (input for dynamic gate management, input for autonomous vehicles, input for predicting delays)
Lufthansa, Southwest, Cathay Pacific, and many other Airlines…
I met plenty of airlines that already use data streaming in production for different scenarios. Fortunately, a few of these airlines were happy to share their stories in the public:
- Southwest Airlines (Data in Motion Tour 2024 in Dallas): Single pane of glass with the ability to view all flight operations and sync their three key schedules: aircraft, passengers, workforce.
- Cathay Pacific (Data in Motion Tour 2024 in Singapore): Rebranded to Cathay because of transitioning from focus on passenger transport to adding cargo and lifestyle / shopping experiences.
- Lufthansa (Webinar 2023): Operations steering, IT modernization (from MQ and ESB to Confluent), and real-time analytics with AI/ML.
The Lufthansa success story is available in its own blog post (including video recording). For even more examples, including Singapore Airlines, Air France, and Amadeus, check out the overview article “Apache Kafka in the Airline, Aviation and Travel Industry“.
Apache Kafka and Flink as Data Fabric for Operational and Analytical Airport Use Cases
Schiphol Group’s vision of an autonomous Amsterdam Airport in 2050 shows where the aviation industry is going: Automated business processes, continuous monitoring and processing of IoT infrastructure, and data-driven decision making and passenger experiences.
Airports like Amsterdam, similarly like airlines such as Lufthansa, Southwest or Cathay, modernize existing IT infrastructure, transition to hybrid cloud architectures, and innovate with new use cases (often learning from other industries like financial services, retail or manufacturing).
Data Streaming with Apache Kafka and Flink plays a crucial role in this journey. Data processing at any scale to provide consistent and good quality data in real-time enables any downstream application (including batch and API) to build reliable operational and analytical systems.
How do you leverage data streaming with Kafka and Flink in the aviation industry? Let’s connect on LinkedIn and discuss it! Stay informed about new blog posts by subscribing to my newsletter.
