Introduction
Commercial Data Warehouse Migration to AWS enables airlines to modernize legacy data environments and support scalable, data-driven decision-making. Many aviation organizations operate complex data warehouse ecosystems built on legacy BI stacks and tightly coupled systems. Over time, these environments become difficult to maintain, expensive to operate, and slow to support new analytics initiatives.
This case study highlights how a large European airline modernized its commercial data ecosystem by migrating its legacy Data Warehouse (DWH) to a cloud-native architecture on AWS. By transforming the legacy platform into a scalable Data Lake and simplifying commercial data workflows, the airline improved data quality, enhanced governance, and enabled faster decision-making across pricing, revenue, marketing, and sales domains. As a result, the organization established a future-ready data platform capable of supporting advanced analytics and AI-driven innovation.
Customer
The customer is a major European airline managing large volumes of commercial and operational data across multiple business units. The airline’s legacy commercial Data Warehouse had grown overly complex due to years of system dependencies, custom pipelines, and fragmented reporting environments.
These challenges made it difficult to maintain data quality, slowed analytics initiatives, and increased operational costs. Therefore, the airline required a modern, cloud-based data architecture that could simplify the commercial data landscape while enabling scalable analytics capabilities.
Business Objective
The primary objective was to modernize the airline’s commercial data ecosystem by migrating its legacy Data Warehouse to a scalable cloud-native Data Lake on AWS.
Key objectives included reducing dependency on legacy BI tools and high-cost infrastructure, simplifying the commercial data landscape, and eliminating interdependency-driven bottlenecks. In addition, the airline aimed to improve data quality and governance while enabling cross-domain visibility across commercial functions.
Another important goal was to support faster decision-making through self-service analytics and unified reporting. Ultimately, the airline sought to establish a future-ready data platform capable of supporting additional business domains and advanced analytics initiatives.
Scope of Services
Platform Modernization & Migration
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Re-engineered the legacy commercial domain DWH into an AWS-native architecture
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Built a scalable Data Lake using S3, Redshift, Spark, Hive, and NiFi
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Migrated complex data pipelines while resolving functional and process interdependencies
End-to-End Reference Architecture
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Designed a cloud-first architecture optimized for analytics, storage, and compute
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Implemented modular processing layers for ingestion, transformation, and data consumption
Functional Review & Business Rule Redesign
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Conducted functional assessment across pricing, revenue, sales, and marketing processes
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Rationalized and redesigned business rules to eliminate redundancies
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Standardized KPI definitions across commercial units
Governance & Quality Framework
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Implemented data quality, metadata management, and lineage tracking
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Established governance workflows and role-based data access
Reporting & Insights Enablement
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Enabled self-service analytics and reporting for commercial teams
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Consolidated insights across pricing, demand, marketing, and revenue domains
Benefits
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Significant cost savings through consolidation of technology infrastructure
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Simplified reporting environment enabling faster insights
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Reduced dependency on IT teams through self-service analytics
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Improved data quality and governance through enterprise frameworks
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Streamlined business rules eliminating complex interdependencies
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Unified commercial data repository supporting cross-functional analytics
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Flexible platform capable of onboarding new business domains
Impact
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40–60% reduction in operational overhead after eliminating legacy systems
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Faster insight generation through self-service access for revenue and pricing teams
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Improved commercial decision accuracy through standardized KPIs
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Analytics project lead time reduced from weeks to days
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Future-ready data platform enabling AI and machine learning use cases
