For generations, organizations leveraged Enterprise Data Warehouse (EDW) systems to consolidate, store, analyze, and report business intelligence. A vendor who had launched the first parallel data warehousing system and the first DW system over 1TB in scale has had success delivering the hardware, software, and services that enabled large-scale on-premises data warehouse solutions for decades. These systems, though extremely costly to purchase and maintain, provided a business advantage to those organizations that could afford it. Today, the exponentially increased variety, volume, and velocity of data call for a data warehouse solution that is more agile, global, and cost-effective. Those who have previously relied on this vendor’s solutions are faced with a question: continue to make investments in rigid, on-premises solutions, or face the one-time cost of migrating to an agile, cloud-based EDW solution.
41-52% Reduction in TCO when migrating EDW deployments"
ESG created a three-year total-cost-of-ownership (TCO) model that compared the expected costs and benefits of upgrading an on-premises EDW solution from the leading vendor mentioned above, migrating to a cloud-based solution provided by this vendor on AWS, or redesigning and migrating the EDW function to Google BigQuery. The costs and assumptions used in this modeled scenario were validated through in-depth interviews with organizations that had previously migrated their operations off of the legacy on-premises EDW solution and into BigQuery. ESG found that our modeled organization can reduce their overall three-year costs by 52% when compared to the on- premises solution and by 41% when compared to the solution on AWS. The elimination of the hardware investment and related operations and maintenance costs contributed largely to the decrease. Moreover, ESG also saw that the underlying architecture of Google BigQuery, decoupling processing capability and storage capacity, also contributed to lowering overall expenses.
