As companies are modernizing their data architectures and moving from traditional data warehouses to data lakes, many are looking at a cloud data lake for their scalability and cost-effectiveness. A leading pharmaceutical company wanted to create a next-generation data architecture by migrating their current data warehouse to the cloud and augment it with a cloud-based data lake on AWS. A key requirement was centralized management and governance of data across both the cloud-based data warehouse and data lake to create a next-generation data platform. 

Challenge: Alexion develops leading pharmaceuticals to address pressing therapies, which often necessitate managing vast amounts of disconnected data coming from a variety of sources. The company wanted to migrate to their current data warehouse to cloud and build a new data lake in the cloud to create their next-generation data platform but were running into trouble trying to build the platform internally due to the complexity of the big data ecosystem. They needed a unified solution that could manage both their data warehouse and data lake.  

Solution: Zaloni helped the company create its next-generation data platform by building a data lake with Amazon EMR and used Amazon Redshift to serve as its data warehouse. Using Arena, Alexion was able to ingest, manage, and govern their next-generation platform while providing role-based access to business users through an enterprise-wide data catalog. The company also decided to leverage Zaloni to manage the platform moving forward.  The following diagram outlines the solution reference architecture the company used for a cloud-based ecosystem.

Results: The company built and deployed a governed enterprise-wide next-generation data platform within weeks that was flexible and responsive while maintaining minimal operational costs. Through the self-service data catalog, the company enabled and empowered a diverse group of stakeholders within the organization to leverage data assets for actionable insights. They dramatically accelerated their time to analytics while reducing the time burden placed on IT.