Business Intelligence: In-Memory Analysis Briefing

Sept. 12, 2010 - PRLog -- (No Email Required)

Direct Link --> http://www.yellowfinbi.com/Document.i4?DocumentId=113381

Extract

Benefits

1. Column-oriented systems are more efficient when an aggregate needs to be computed over many rows but only for a notably smaller subset of all columns of data, because reading that smaller subset of data can be faster than reading all data.

2. Column-oriented systems are more efficient when new values of a column are supplied for all rows at once, because that column data can be written efficiently and replace old column data without touching any other columns for the rows.

3. Data compression - Column data is of uniform type; therefore, there are some opportunities for storage size optimizations available in column-oriented data that are not available in row oriented data.

Data types supported by the In-Memory Database

The Yellowfin in-memory database is designed to allow you to access the following data types for analysis and reporting.

* All standard numeric data types (eg. int, decimal, float, binary)
* All standard character data types (eg. char, nchar, varchar, nvarchar, text)
* All standard time data types (eg. timestamp, datetime)
* GIS data (GIS points, GIS polygons)
* BLOB & CLOB data

# # #

About Yellowfin

Yellowfin is passionate about making Business Intelligence easy. Recently recognized among 25 rising companies that CIOs must know about, Yellowfin is a leading web-based BI solution that can be easily integrated into any third-party application or delivered as a stand-alone enterprise platform.

For further information please contact:
Paul Stubbs
Pre-Sales Manager, Yellowfin
Direct line: +61 3 9090 0455
Email: paul.stubbs@yellowfin.bi

www.yellowfinbi.com

More Information

Mobile BI
http://www.yellowfinbi.com/Document.i4?DocumentId=99556

Case Study
http://www.yellowfinbi.com/Document.i4?DocumentId=103521

Video
http://www.youtube.com/user/YellowfinTeam