The data warehouse has served as the foundation for analytics architecture for the past 30 years. But the era of big data has rolled over many of the traditional approaches to delivering data and insights to business users. Consequently, some think the data warehouse has become obsolete, while others believe it still plays a key role in a modern analytics ecosystem.

This report explores the promise and reality of both traditional data warehouses running on relational databases and data lakes running on Hadoop. Each environment has unique capabilities that make it ideal for supporting different kinds of use cases and workloads. This report examines the strengths and weaknesses of both environments and shows how they can complement one another in a modern analytics ecosystem.

Organizations today—especially those with large volumes of multi-structured data—are pairing data warehouses and data lakes to create a vibrant information supply chain that continuously feeds a multiplicity of downstream systems and applications. The result is a modern analytics ecosystem that adapts quickly to new requirements while hiding the complexity of the data environment from business users and many developers.