As Budapest’s unified transport authority, BKK manages the public transit, shared mobility, infrastructure, and traffic systems that keep the city of 1.7 million people moving. “We are like a think tank for strategic transportation in the city,” says Max von Münster, BKK’s Lead Data Science Expert. The organization collects large datasets from 2,000+ buses, hundreds of metros and trams, and nearly 1,000 shared bikes and scooters, tracking locations, vehicle speeds, passenger counts, and more. 

Its legacy on-premises data warehouse struggled to keep up with the growing volume and variety of data, making it difficult to access and analyze information efficiently. BKK needed a way to centralize and democratize its datasets while modernizing the platform to support advanced geospatial analytics and machine learning.

Building a modern transit analytics platform

Before Databricks, BKK used on-premises Microsoft SQL servers and fragmented Excel- and PowerBI-based reporting. Analysts could query GPS or boarding data only by extracting subsets into Jupyter notebooks, and large datasets strained the system.

“The city is full of decentralized information,” noted Max. “One of our big goals is to bring that together in the cloud and make it accessible to the experts who make the decisions.” 

To address this, BKK began a phased migration to Azure Databricks. Mobility data came first due to its volume and importance for planning and geospatial analytics. Datasets sourced from vehicle GPS, passenger sensors, boarding schedules, and a wide variety of other systems were migrated with a focus on careful modeling and validation in the cloud.

“It’s really important to connect these decentralized sources,” said Business Intelligence Analyst Estilla Híves. “Different teams often need similar insights from different perspectives, and with a centralized platform we can combine the data and share it across teams.” 

Abylon, a Databricks partner, played a key role in accelerating BKK’s cloud data transformation. By helping BKK develop its Azure-based data platform, enabling its data warehouse and data operations in the cloud, and guiding the organization into the Databricks ecosystem, Abylon laid the foundations for a scalable, long-term cloud data journey.

Unlocking new analytics and modeling use cases

With the Databricks Lakehouse architecture, BKK can explore and act on data in ways that were previously impractical. Analysts can now process large, complex datasets efficiently using the full power of the cloud, providing faster insights and more responsive operational decisions. The platform is also far easier to use than their previous system; analysts can work seamlessly in SQL, Python, or R within the same collaborative notebooks, sharing work across teams without needing to transfer variables or data objects.

These improvements have unlocked a range of powerful, real-world use cases, including:

• Minute-level tracking of shared mobility vehicles: BKK maps over 900 scooters and bike-sharing stations every minute, monitoring parking needs and directing allocation decisions
• Route-level performance analysis for public transit: GPS and speed data from buses and trams highlight slow segments, helping optimize traffic light timing and route planning
• Predictive modeling for airport buses: BKK combines live airport API data with long-term demand forecasts to update bus schedules in real time and plan for passenger demand through 2033
• Dynamic scheduling and capacity: BKK uses ridership patterns, seasonal trends, and event data to adjust bus and tram schedules to prevent overcrowding 

These innovations give BKK the ability to act faster, make smarter choices, and plan proactively for city-wide mobility.

Empowering the future of urban transportation

Databricks positions BKK to expand analytics across departments and collaborate more broadly. The platform supports data democratization, providing governed access to mobility datasets for internal experts today and creating the foundation for future collaboration with external partners and even other European cities.

Max highlighted the long-term vision: “We aim for a digital twin of Budapest’s mobility system to analyze patterns, simulate scenarios, and continuously improve public transport.”

The benefits extend beyond city-wide mobility. Databricks also gives BKK detailed cost tracking, team-level visibility, and tools to optimize internal resource allocation. “We can differentiate between teams and track who is generating compute costs, which is very useful for budgeting and planning,” said Estilla.

By combining these internal efficiencies with smarter, data-driven transit operations, BKK is moving from reactive reporting to proactive city planning — unlocking insights that were previously impossible and setting the stage for the next decade of urban mobility innovation.