The client

LOT Polish Airlines is a national air travel operator, founded in 1929 and headquartered in Warsaw. Actively operating for nearly 100 years, the airline is one of the longest-existing in the world. Offering numerous international and domestic connections, LOT has become an important player in the aviation market, connecting Poland with many destinations around the world.

Nowadays, LOT is implementing a revised growth strategy, aiming to increase the fleet by 50% (110 airplanes in 2028) and passenger capacity by around 70% (16.9 million by 2028).

How can LOT leverage data as a strategic asset to drive efficiency, enhance customer experience, and fuel its growth strategy?

The challenge

Our client leverages the Lufthansa Netline HUB system to streamline passenger transfer processes at Chopin Airport in Warsaw. Despite the critical role of this system, its limited reporting functionalities restrict comprehensive data analysis over extended periods. This limitation complicates strategic decision-making, particularly in refining the complaint-handling process—an essential factor in enhancing passenger satisfaction and reducing complaint volumes.

The challenge lies in augmenting the capabilities to provide deeper insights and support more informed decisions, thereby aligning operations with the airport’s goal of elevating passenger experience.

The solution

In response to the client’s requirements, we recommended and implemented a robust data platform integrated with the Netline HUB system to collect and process data for enhanced analysis and reporting.

This solution established a foundation for an enterprise-class data platform designed to accommodate additional data sources in the future. It is strategically positioned to assume the role of the central information management system within the enterprise, thus aligning with long-term business objectives.

Technical approach

Data ingestion and transformation

Under the implemented solution, we designed a scalable, flexible Azure cloud platform. The aim of creating a solution based on Databricks technology enabled to gather, ingest and analyze historical data from the source system. The platform provided LOT employees with unrestricted access to historical data.

Cost reduction and process optimization

One of the technical challenges was the lack of performance and cost loading of the current source system. To address this need and provide LOT with effective optimization and cost reductions, the implemented solution was based on the Data Lakehouse architecture. Relying on this type of lakehouse architecture provided not only a financially and performance-optimized platform but also ensured a standardized, high-quality data management platform.

Data integrity and quality assurance

A further challenge addressed by the client included the lack of standardization and consolidation of existing data located in the source system. The capacity of the client’s raw data was faced with ununification and minor standardization of the format. To convert LOT data productively, the implemented solution developed transformational scripts into a standardized, tabular format. By using Python and Spark SQL, the solution covered not only data configuration and transformation, but also provided a high-quality data-driven management of implemented platform.

Standardization and unification of data was also ensured by the implementation of a generic automated mechanism. The metadriven CI/CD data pipeline with the use of Azure DevOps improved client’s ability to manage the configuration of the data transformation conveniently and flexibly. Additionally, the generated solution is equipped with the possibility to be extended cost-effectively in the future. This guaranteed the client comprehensive maintenance and quality assurance, even after the project has run its course.