A Leading U.S. Hospitality Brand Achieved 30% Cost Savings and Retired Legacy Systems Using Azure-Based Data Warehouse Platform
Overview
The client is a premier hospitality brand in the United States, renowned for its luxury resorts, world-class hotels, and unmatched entertainment experiences. Serving millions of guests annually, the organization thrives on delivering exceptional service while driving operational efficiency. As the business expanded, data became a strategic asset for optimizing customer experiences, streamlining operations, and unlocking new revenue opportunities. Recognizing the limitations of its legacy systems, the client sought to modernize its data infrastructure to support agile decision-making and future growth.
The client’s 20+ year-old Teradata Data Warehouse—spanning 100+ databases and over 100,000 ETL scripts—had become a costly bottleneck for innovation. Key issues included:
Escalating maintenance and license costs are draining IT budgets
Outdated architecture restricting scalability, agility, and adoption of modern analytics capabilities
Absence of a cloud-first strategy prevents data consolidation and reuse of assets
A fragmented data landscape is slowing down insights and operational reporting
Heavy reliance on manual processes for data cleansing and transformation
These challenges inflated costs and impacted the brand’s ability to deliver timely, insight-driven guest experiences.
Solution
We delivered a next-generation Azure-based Data Warehouse Platform designed to maximize operational efficiency, reduce costs, and accelerate time to insight.
Solution Highlights:
Built a reference architecture leveraging Azure Data Lake Storage (ADLS) and SQL DWH for a unified, cloud-first data platform
Deployed IngestXpress accelerator to automate ingestion, schema migration, and SQL query transformation from Teradata to SQL DWH
Leveraged Azure Data Factory (ADF) and Databricks for ingestion and high-performance Spark-based ETL
Adopted a hybrid migration approach using DataStage and Datometry for a seamless, low-disruption transition
Utilized Databricks for data transformation, cleansing, profiling, and performance optimization
Consolidated multiple legacy DWH environments into a single source of truth for enterprise analytics
The Impact
30% cost savings achieved through automation, optimized cloud infrastructure, and license retirement
25% faster migration timelines with accelerator-driven delivery
Millions saved in legacy license costs, freeing funds for innovation projects
Retired all Teradata instances and unused scripts, reducing technical debt
Delivered a future-ready analytics ecosystem, empowering data-driven decision-making and competitive advantage
