Airline operations generate massive volumes of operational data from flight systems, crew management platforms, and real-time event sources. Accurately processing this data is critical for fair compensation, regulatory compliance, and timely operational decision-making, especially for complex use cases such as Flight Attendant (FA) Boarding Pay calculations.
As data volumes and complexity increase, airlines are shifting from manual transformations to scalable, cloud-native data platforms that can process high-frequency operational data reliably, accurately, and at speed.
The Challenge
The airline group needed to ingest and process large-scale, complex JSON datasets from multiple operational systems to compute FA Boarding Pay accurately and support compliance reporting. Existing manual transformation processes introduced delays, inconsistencies, and errors, impacting the reliability of boarding pay and recovery calculations.
Disconnected pipelines and a lack of optimized, scalable processing made it difficult to track boarding events accurately and generate analytics-ready datasets in a timely manner.
The client required a structured, automated approach to convert unstructured operational data into validated, consumption-ready formats.
Our Approach
Coforge implemented a structured, multi-layered cloud data processing architecture using Databricks and Azure Data Lake, enabling scalable ingestion, schema enforcement, and automated transformations.
Cloud‑Native Data Processing Architecture
Designed an end‑to‑end Databricks and Azure Data Lake architecture optimized for efficient ETL processing of large, complex JSON datasets.
Analytics & Reporting Enablement
Enabled analytics and reporting pipelines that provide ready‑to‑consume data for operational analysis and regulatory compliance, while ensuring scalability, reliability, and performance.
Multi‑Layer Data Foundation
Established a layered architecture to ensure data quality, traceability, and analytics readiness:
- Landing Zone for ingestion from multiple operational sources using the DataFlux artifact
- Raw Layer to store unprocessed JSON data
- Struct Layer for schema enforcement and structured transformations using the ETL Script Converter accelerator
- Prep Layer to apply business rules and filtration logic
- Packaged Layer delivering optimized datasets for analytics, compliance, and reporting

Impact to Date
~40% Reduction
~35% Faster
Business Impact
- Scalable and Automated Data Pipelines Replaced manual, error‑prone processes with a robust, cloud‑native ETL architecture.
- Improved Data Reliability Ensured structured, validated data flows for accurate compensation and compliance use cases.
- Faster Operational Decision‑Making Delivered analytics‑ready datasets that support timely insights into boarding events and pay calculations.
- Future‑Ready Data Foundation Established a flexible architecture capable of supporting additional operational and analytical use cases.
By implementing a scalable, multi-layered cloud data processing architecture, Coforge enabled the airline group to automate FA Boarding Pay calculations, improve accuracy, and deliver timely compliance insights, driving operational efficiency and data confidence at scale.
