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Faster SQL Profiling for Better Database Performance
sponsored by Embarcadero Technologies, Inc.
WHITE PAPER: Find out how Embarcadero's DB Optimizer™ can help you to quickly discover and fix problematic SQL statements that can cause performance bottlenecks in your database. DB Optimizer™ gives you the flexibility of using a single easy-to-use IDE to test, profile and tune SQL.
Posted: 16 Apr 2009 | Published: 16 Apr 2009

Embarcadero Technologies, Inc.

How Pink OTC Markets Cuts Reporting Time and Operating Costs with a Market Data Warehouse Built on Vertica and Syncsort
sponsored by Vertica Systems
WHITE PAPER: Learn how Pink OTC Market Inc., the third largest U.S. equity trading marketing place,  built a highly available and reliable (no downtime in a year of production use) data warehouse using Vertica's Analytic DBMS that cost-effectively stores billions of records and scales easily by simply adding CPUs without incurring additional licensing fees.
Posted: 16 Jul 2010 | Published: 16 Jul 2010

Vertica Systems

Full Demo: IBM Cognos 8 Business Intelligence
sponsored by IBM
PRODUCT OVERVIEW: Complete capabilities in one product, on a single architecture. See key features for both business and IT users, including Framework Manager, the Administration Console, and integration with Microsoft Excel.
Posted: 06 Oct 2009 | Published: 06 Oct 2009

IBM

Fast Growing Company, Mayflex, Chooses IBM Cognos Express to Deliver Essential Business Intelligence and Planning Capability
sponsored by IBM
CASE STUDY: Read about how Mayflex used IBM Cognos Express to replace a complex spreadsheet system, improve time-consuming budgeting and forecasting processes, compare actuals against budgets at various levels and save time for end-users through automatic system updates.
Posted: 08 Feb 2011 | Published: 08 Feb 2011

IBM

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

Choosing a Standard for BI and Reporting: Simplify the IT Environment Reduce Your TCO Increase Your ROI
sponsored by IBM
WHITE PAPER: The reasons driving standardization in BI and reporting are core to every company—cutting costs, boosting revenue, and increasing profits. IBM Cognos 8 BI lets business users move among capabilities in a guided way, get the answers they seek, and collaborate while giving IT complete control over the BI environment.
Posted: 05 Oct 2009 | Published: 05 Jan 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

SAP NetWeaver Enterprise Search: Easy and Secure Information Access
sponsored by SAP*
WEBCAST: Learn how you can open up new ways to interact with business content and applications from across the enterprise with NetWeaver Enterprise Search.
Posted: 02 Aug 2007 | Premiered: Aug 2, 2007, 09:00 EDT (13:00 GMT)

SAP*

TDWI Checklist Report: Data Requirements for Advanced Analytics
sponsored by SAS
WHITE PAPER: This checklist lists and explains data requirements that are unique to advanced analytics. This report focuses on the data requirements of advanced analytics so organizations may become better equipped to populate a data warehouse or analytic database with data and data models that ensure the success of advanced analytic applications.
Posted: 07 Apr 2010 | Published: 07 Apr 2010

SAS

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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