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Ad Hoc Query Tools White Papers (View All Report Types)
 
Data warehouse augmentation: the queryable data store
sponsored by IBM
WHITE PAPER: This white paper highlights how organizations can leverage big data to solve business challenges. It focusses on queryable data stores as a way to reduce costs and gain business insights.
Posted: 06 May 2014 | Published: 31 Dec 2013

IBM

Understanding the Components of Business Confidence
sponsored by IBM
WHITE PAPER: With over 60% of CEOs saying they need to do a better job of capturing and understanding information to make more effective and timely business decisions, it's clear that there's a gap between capability and need. It's similarly fair to say that this shortfall is a leading reason why businesses aren’t as competitive as they’d like to be.
Posted: 08 Oct 2009 | Published: 08 Jul 2009

IBM

Ovum Technology Review of Vertica’s Analytic Database
sponsored by Vertica Systems
WHITE PAPER: Column-based databases can be slow deleting and updating data. Vertica’s Analytic Database is designed specifically for storing and querying large datasets. Vertica’s differentiator is that it combines a columnar database engine with MPP and shared-nothing architecture, aggressive data-compression rates, and high availability.
Posted: 16 Jul 2010 | Published: 16 Jul 2010

Vertica Systems

Nucleus Research Note: IBM Cognos Express
sponsored by IBM
WHITE PAPER: Learn how midsize companies can rapidly adopt Business Intelligence (BI) and Performance Management (PM) technologies while minimizing training, support and ongoing costs in this research note from Nucleus Research.
Posted: 17 Feb 2011 | Published: 17 Feb 2011

IBM

Selecting a Visual Analytics Application
sponsored by Tableau Software
WHITE PAPER: Visual analytics is the process of analytical reasoning facilitated by interactive visual interfaces. Its becoming the fastest way for people to explore and understand Business Intelligence data of any size. This paper will introduce you to the seven essential elements of true visual analytics applications.
Posted: 20 Apr 2009 | Published: 20 Apr 2009

Tableau Software

Sybase IQ , A Competitive Analysis
sponsored by Sybase, an SAP company
WHITE PAPER: This paper, intended for both IT and operational management, explores a series of high-level best practices for analytics and data warehousing based on Sybase’s experience with successful implementations. Suggestions are provided in three categories: performance, cost control, and ease-of-deployment with each dimension broken down into sub-topics.
Posted: 28 Oct 2010 | Published: 28 Oct 2010

Sybase, an SAP company

Break the “Add More Nodes” Cycle: IBM Netezza data warehouse appliances allow your data infrastructure to scale
sponsored by IBM
WHITE PAPER: Data warehouse appliances have transformed the DW with a platform designed to deliver optimal price and performance with extreme simplicity. This paper outlines the IBM Netezza data warehouse appliance and how it offers extreme time to value, agility and ability to grow with new workloads, cost reduction of ongoing maintenance and more.
Posted: 05 Apr 2012 | Published: 14 Dec 2011

IBM

Query Offloading for Improved Performance and Better Resource Utilization
sponsored by Oracle Corporation
WHITE PAPER: This white paper focuses on the use of a log-based, real-time change data capture (CDC) solution to enable real-time reporting using a low overhead solution that minimizes the impact on IT infrastructure.
Posted: 02 Feb 2010 | Published: 04 Jan 2010

Oracle Corporation

Sybase IQ: The Economics of Business Reporting
sponsored by Sybase, an SAP company
WHITE PAPER: Reporting is recognized as a way for companies to improve service, ensure quality, control costs, and prevent losses by empowering decision-makers throughout the organization. This paper will explain why reporting is one form of business intelligence that has become business-critical.
Posted: 31 Aug 2009 | Published: 01 Aug 2009

Sybase, an SAP company

Top 10 Data Mining Mistakes
sponsored by SAS
WHITE PAPER: In the following paper, we briefly describe, and illustrate from examples, what we believe are the “Top 10” mistakes of data mining, in terms of frequency and seriousness. Most are basic, though a few are subtle. All have, when undetected, left analysts worse off than if they’d never looked at their data.
Posted: 07 Apr 2010 | Published: 07 Apr 2010

SAS
 
 
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