Case Study

Enabling Real-Time Customer Intelligence Through Cloud Data Modernization

Industry

Travel, Transportation & Hospitality (Airlines)

Location

United States

Our Contributions

Cloud Data Modernization, Customer Intelligence Platforms, Real-Time Data Processing, Data Engineering & ETL Automation, Analytics Enablement, Data Governance

Airlines operate in a highly competitive environment where customer experience, personalization, and operational efficiency are driven by data. However, legacy on-premises data platforms, often built across reservations, ticketing, and loyalty systems, are unable to deliver the speed, scale, and integration required for real-time customer intelligence.

To unlock actionable insights and enable data-driven engagement, airlines are modernizing their data estates with cloud-native platforms that consolidate historical data, support near-real-time analytics, and empower business teams with self-service insights.

The Challenge

The airline’s customer and operational data were fragmented across multiple legacy platforms supporting reservations, ticketing, loyalty, and engagement systems. Large volumes of historical data and complex ETL processes slowed modernization and limited timely access to insights.

Disconnected pipelines across vendors prevented a unified view of the customer, while long ETL execution cycles delayed analytics and decision-making. Limited automation and validation increased the risk of data inconsistencies, and heavy reliance on manual processes restricted self-service analytics for business teams.

Our Approach

Coforge implemented a cloud-based data platform to modernize legacy systems and enable real-time customer intelligence across the airline’s data ecosystem.

Cloud Data Platform Modernization

Migrated legacy on-premises workloads to an AWS-based analytics platform using Amazon Redshift, consolidating large volumes of historical and operational data into a scalable, centralized environment.

Automated Ingestion & Real-Time Processing

Enabled automated ingestion and processing pipelines using AWS Glue, Kinesis, and Spark, significantly reducing ETL execution times and supporting near real-time data availability.

High-Volume Data Transformation

Migrated and transformed 14TB of customer data, modernizing 300+ ETL processes and processing 7B+ historical records efficiently within the cloud environment.

Data Quality, Modeling & Self-Service Analytics

Introduced automated data validation and monitoring to enhance reliability. Implemented an industry-aligned travel data model to standardize customer, booking, and operational datasets, and enabled self-service analytics using Tableau and Alteryx.

Impact to Date

The cloud data modernization initiative delivered measurable improvements in processing speed, insight availability, and analytics adoption.

14TB Migrated

Enterprise Data Modernization

7B+ Records Processed

Historical Data Transformation

3M+ Records Daily

Near Real-Time Analytics