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Introduction to Sybase IQ Multiplex Grid Option
This video introduces Sybase IQ's Multiplex Grid Option that enables highly parallel and scalable framework for enterprise data warehousing

Sybase IQ Multiplex Grid Option

The Sybase IQ Multiplex Grid Option enables users to incrementally scale-out concurrent query jobs, load jobs, or both in a PlexQ grid. With Distributed Query Processing, users can scale-out an individual query across many nodes in a PlexQ grid. Logical servers allow IT staff to band together compute resources in a PlexQ grid into virtual groups in order to isolate the impact of different workloads and users from each other. Users have the flexibility to independently and easily add servers to a PlexQ grid, or disk space to the SAN. Licensed as Sybase IQ Multiplex Grid Option, a PlexQ grid provides linear, incremental scalability to your analytics environment.

Sybase IQ Multiplex (Technical Overview)
This technical overview outlines the Sybase IQ Multiplex and its “shared everything” architecture, called “PlexQ”, where all compute nodes interact with the same shared storage, and queries can distribute across all the nodes.

Sybase IQ Multiplex: Delivering Elastic Capacity for High Performance Enterprise Data Warehousing (Technical Whitepaper)
Ever-increasing data volumes coupled with growing demand for trustworthy knowledge has led to increased investment in business intelligence technologies as new analytic and BI applications are built and deployed. Consequently, there is a corresponding expectation for increased analytics capacity that goes beyond traditional reporting. Along with the desire to optimize expenditures, maximize revenues, and adjust business models and processes, the demand for different types of business intelligence systems, reporting, and analytics must contend with limits on existing budgets, hardware, and services. This growth is powered by factors such as larger user communities, more data, the need for deeper insight and additional detail, all delivered to the right people in near real-time.


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