In-database analytics: accelerate decisions, improve performance and resource utilization

Analytics and predictive modeling are traditionally done on a server or analyst's workstation after importing all of the data or analysis to that host. The need for in-database analytics has emerged as computational resources of the database platforms have increased to provide more capacity for data processing. At the same time, the speed of connections (network switches) between storage devices and computational servers is constantly increasing, providing various options for how and where to perform analyses of big data.

This paper discusses Statistica's native database-agnostic approach to in-database analytic processing. Statistica has traditionally supported external analytics libraries, analytic and algorithm marketplaces, and platforms (through Statistica Native Distributed Analytics Architecture, NDAA). The platform provides a wide range of options for moving analytic algorithms to the data or edge and can leverage the leading open-source libraries for in-memory analytics via Spark. This white paper specifically focuses on the discussion of trade-offs and options for query-based in-database analytics using Statistica and the key factors to consider before choosing a specific approach for big data analytics.

Download White Paper

要处理您的注册,TIBCO Software Inc.和 TIBCO附属公司 (统称“TIBCO”)需要收集以下您的个人信息。通过注册TIBCO 资源, 即表示您同意TIBCO处理此信息并通过电子邮件,电话和/或社交媒体与 资源相关的信息与您联系。