Case Study
Azure Data Lakehouse Enables Higher-Visibility Reporting Across Data Sources
- Customer: St. Luke’s University Health Network
- Challenge: Centralize data in a modern data platform to enable higher-visibility reporting across disparate data sources
Results
- Implemented Azure data lakehouse ahead of schedule
- Optimized data loads, run times, and cost savings
- Elevated training of client engineering teams and project documentation
Tegria has exceeded expectations in modernizing our data platform. The transition to an Azure data lakehouse has been seamless, and the project was completed ahead of schedule. This allowed for additional optimizations and thorough training of our teams. With Tegria’s help, we have a scalable and efficient data infrastructure that supports our current needs and positions us well for future growth.
DR. CHARLIE SONDAY, DNPAssociate Chief Medical Information Officer, St. Luke's University Health Network
Background and Challenge
St. Luke’s University Health Network (SLUHN) is a nonprofit, fully integrated network providing services at 15 campuses and more than 300 sites in Pennsylvania and New Jersey. This nationally recognized network has provided cost-effective and high-quality care to patients, regardless of their ability to pay, for more than 150 years. Due to demand for higher-visibility reporting across disparate data sources, SLUHN needed to centralize its data in a unified, cost-effective modern data platform.
Solution
SLUHN partnered with Tegria to implement a cloud-based Azure data lakehouse, which is a data management system that combines the benefits of data lakes and data warehouses. This architecture helps support current end-user needs and provides the infrastructure for future development and ingestion of additional data sources.
Tegria utilized a two-part methodology to help SLUNH implement this highly scalable, efficient modern data platform:
- Architect and deploy a medallion layer Azure Modern Data Platform within SLUHN’s Azure tenant, providing data extraction for an initial set of six data sources into an Azure Data Lake Storage Gen2 account
- Develop ingestion pipelines in Azure Data Factory and utilize Azure Databricks’ notebooks to consume backfill, incremental data loads, and streaming data loads into delta lake tables within Azure Databricks unity catalog
Results
True partnership and commitment to success enabled Tegria and SLUHN to complete the setup and configuration of the Azure data lakehouse architecture ahead of schedule. Early completion of the project empowered the team to further optimize data loads, run times, and cost savings. It also unlocked opportunities for ongoing improvements and resource optimization. Tegria experts used the surplus of time to elevate training of SLUHN engineering teams, enhance project documentation, and prepare a seamless transition to future endeavors.