Enterprise data warehouses (EDWs) have existed for about 20 years, serving as the foundation of insight-driven organizations, delivering timely analysis and reporting of structured data; handling large analytics workloads; and supporting the high levels of concurrency that organizations demand. But while EDWs have been a familiar presence in many organizations, as companies look to reduce their data center footprints, increase organizational agility, and incorporate as much data as possible into their analytics workflows, the architectural rigidity, complexity, and cost of traditional EDWs have paved the way for modern data warehouses to better respond to the dynamic needs of the business.
