Over the past 50 years, RAM, disk space, and processing capabilities have become progressively cheaper and faster. Now, with an ever-faster migration to cloud-based computing, processing and storage resources have become effectively limitless, offering tantalizing analytic potential.
The demand for ever-sophisticated insights has driven organizations to pursue tools that can query these vast datasets without sacrificing speed or dexterity. However, the risks of incomplete or poorly designed self-service BI architecture and governance programs have become evident. How can the same level of analytic power be brought to bear against wildly different underlying storage options, completely transparent to most business users?
By abstracting the analytic capabilities from the underlying storage systems, Pyramid effectively solves this with an In-Place Analytics solution. Along with a scalable, enterprise-class analytic environment that can operate without compromising analytic power across a wide range of underlying data storage engines, In-Place Analytics gives users:
This white paper describes the rise of In-Place Analytics to show how this new data and analytics paradigm allows organizations to dramatically increase analytic query performance, leverage the full power of existing analytic models, and perform analytic calculations—from basic to advanced—via a direct query into the underlying data storage engine without ingesting the data into a proprietary analytic database.