Case Study

Optimizing FA Boarding Pay & Compliance with Scalable Cloud Data Processing

Industry

Travel, Transportation & Hospitality (Airlines)

Location

United States

Our Contributions

Cloud Data Engineering, Databricks & Azure Data Lake, JSON Data Processing, ETL Automation, Compliance Analytics, Scalable Data Architecture

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

The automated data processing platform delivered measurable improvements in efficiency, speed, and data accuracy for FA Boarding Pay and compliance reporting.

~40% Reduction

Manual Effort

~35% Faster

Data Processing