Data warehouses have an older design, which becomes stifling in a world where information and data escalate at an exponential pace. Just try to picture hundreds of hours dedicated to managing infrastructure, fine-tuning the clusters to address the workload variance, and dealing with significant upfront costs before you get a chance to analyze the data. Unfortunately, this is the best that one can expect out of traditional data warehousing methodologies. For data architects, engineers, and scientists, these burdens become a thorn in their side, reducing innovation by 30% and slowing the process of gaining insights from increasingly large data sets by up to 50%.