What is compression/Collapse of Info Cube in SAP-BI
MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 3 Hours | Lec: 10 | 2.37 GB
Genre: eLearning | Language: English

Step-By-Step procedure to Create Info Cube & Apply Compression/Collapse for Info Cube & Why do we need compression

What is Infocube?

For example, a Sales Amount Infocube which has dimensions like MONTH - PRODUCT-CUSTOMER-REGION, can be viewed by any of the axes, for example total sales by region or by customer.

Step-By-Step procedure to Create Info Cube & Apply Compression/Collapse for Info Cube & Why do we need compression
What Is an Infocube in SAP BI/BW? How To Create One?
What is Infocube?
Infocube is data storage area in which we maintain data which we are extracting from source system physically. An InfoCube can function as both a data target and an InfoProvider. From a reporting point of view, an Infocube can be described as a self-contained dataset.
For example, a Sales Amount Infocube which has dimensions like MONTH - PRODUCT-CUSTOMER-REGION, can be viewed by any of the axes, for example total sales by region or by customer. The dimensions of an Info-Cube are entities or hierarchies.
BIW ( Business Intelligence Warehouse) provides facility to define 16 dimensions, out of which 3 are pre-defined. nfoCube Structure:
Type of InfoCube
Infocube is classified in to three types based on the way of maintaining and distributing the data.
Introduction
Even with "high performance hardware" the datavolume in a datwarehouse can get big enough that everyday loaded InfoCubes cause perfromance issues. For that reason bw provides a couple features that help to increase performance. Compressing the fact table is one option that optimized the access to basis infocubes.
What is a compression exactly?
Every InfoCube has a datapackage dimension that holds the request id. This allows bw to store the data in a granularity which is not necessarily required from a business perspective.
Depending on the datamodel of the InfoCube and the frequency of loads as well as the content of the loaded data this can have a significant impact on the datavolume. Further the data is split in multiple packages with each dataload. Those packages do not allow an aggregation and therefore each datapackage is limited within those boundaries.
An aggregation of the data across datapackages by removing the request id can shrink the datavolume of the factable dramatically without any downfall from a business perspective.
To achieve this each InfoCube consist out of two two fact tables one with a request id and one without it. The blue one is the standard fact table with a name either /BI0/F or /BIC/F. The green compressed fact table is named /BI0/E ot /BIC/E.
If you don't do anything specific the E-Table is not generated.
During the compression BW moves data from the normal F-table to the compressed E-Table. By choice the compression can be all or only part of the requests that have been loaded.
The OLAP processor is automatically combining the E and F table during the query.

DOWNLOAD
uploadgig