Difference Between Aggregates and Compression

Hi,
Can you tell me what is the difference between Aggregates and Compression?
I know ,once compressed data is not available for deletion in request wise
and it moves from F table to E table .
Aggregates means ,data will move from cube to aggreagates(Baby cubes).
But my query is ,As both of them aggregates the data.which of them should be used. at what situation?
I hope you understood my Query.
Regards.
Naresh.

Hi,
An aggregate is a materialized, aggregated view of the data in an InfoCube. In an aggregate, the dataset of an InfoCube is saved redundantly and persistently in a consolidated form into the database.
Aggregates make it possible to access InfoCube data quickly in Reporting. Aggregates serve, in a similar way to database indexes, to improve performance.
especially in the following cases we create aggregates:
The execution and navigation of query data leads to delays with a group of queries.
You want to speed up the execution and navigation of a specific query.
You often use attributes in queries.
You want to speed up reporting with characteristic hierarchies by aggregating specific hierarchy levels.
For more info on aggregates go though the link below
https://help.sap.com/saphelp_sem320bw/helpdata/en/c5/40813b680c250fe10000000a114084/frameset.htm
Compression creates a new cube that has consolidated and summed duplicate information.
2. When you compress, BW does a group by on dimensions and a sum on measures... this eliminates redundent
information.
3. Compressed infocubes require less storage space and are faster for retrieval of information.
4. Once a cube is compressed, you cannot alter the information in it. This can be a big problem if there
is an error in some of the data that has been compressed.
For more info go though the below link
http://www.sap-img.com/business/infocube-compression.htm
Regards,
Marasa.

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What is difference betwwen aggregates and compression in bi 7.0.

hi All,
will you please tell me difference between compression and aggregates in bi.

Dear Ganesh,
Aggregates allow you to improve the performance of BI queries when data is read from an InfoCube. The data of a BI InfoCube is saved in relational aggregates in an aggregated form. Relational aggregates are useful if you want to improve the performance of one or more specific BI queries, or specifically improve the performance of reporting on characteristic hierarchies
Compression is phenomenon where all the contents of ur info cube moves from F to E table,,This is called compression and data will be aggregated when this compression happens..
Why do we do compression..??
When you load data into the InfoCube, entire requests can be added at the same time. Each of these requests has its own request ID, which is included in the fact table in the package dimension. This makes it possible to pay particular attention to individual requests. One advantage of the request ID concept is that you can subsequently delete complete requests from the InfoCube.
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You can eliminate these disadvantages by compressing data and bringing data from different requests together into one single request (request ID 0).
For more info on aggregates
http://help.sap.com/search/highlightContent.jsp
and info on Compression
http://help.sap.com/search/highlightContent.jsp
Hope this helps u...
Best Regards,
VVenkat..

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Hi FCP users,
I am actually an audio guy, but I would like an answer on something.
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gazo wrote:
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Hi All,
Is there a relation between rollup and compression ? i.e if i compress the cube, will the rollup job be faster ?

Hi,
Thanks for all your replies. Now the picture is crystal clear.
1. A compression job moves data from F table to E table. This is also applicable for aggregates. If you schedule a compression job for a cube, the aggregates are automatically compressed( You can also compress aggregates of a cube without compressing the cube. There are programs avalibale for the same).
2. Assume that you have not done compression . If you load the data to base cube ,it takes more time to create the indexes after loading.
3) Assume that you have done compression . If you load the data to base cube ,it takes less time than 2 point to create the indexes after loading.
On similar lines, if aggregates of a cube are compressed before rollup, then rollup will be faster. This is what exactly I have experienced. I had a cube which had 1000 request. It was never compressed. The rollup job for the cube used to take around 60,000 seconds. I compressed 900 requests. Now the rollup job gets over in 2400 seconds.So the conclusion is the following:
<b>When aggregates of a cube are compressed, the rollup job runs faster.</b>
Message was edited by: Tej Trivedi

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Regards
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Hi
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Hi ALL,
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afzal wrote:
Hi ALL,
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===================
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hi
anyone plz explain...
1. Difference between collect and move stmts
2. Badi and user exit.
gowri

Hi,
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1. SE18
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1. SE19
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hi navin...
Online transactional processing (OLTP) is designed to efficiently process high volumes of transactions, instantly recording business events (such as a sales invoice payment) and reflecting changes as they occur.
Online analytical processing (OLAP) is designed for analysis and decision support, allowing exploration of often hidden relationships in large amounts of data by providing unlimited views of multiple relationships at any cross-section of defined business dimensions.
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The Impact of the OLAP/OLTP Cultural Conflict on Data Warehousing....check this link....
http://www.georgetown.edu/users/allanr/Impact.pdf
also check...
http://expertanswercenter.techtarget.com/eac/knowledgebaseAnswer/0,295199,sid63_gci977813,00.html
The big difference when designing for OLAP versus OLTP is rooted in the basics of how the tables are going to be used. I'll discuss OLTP versus OLAP in context to the design of dimensional data warehouses. However, keep in mind there are more architectural components that make up a mature, best practices data warehouse than just the dimensional data warehouse.
Corporate Information Factory, 2nd Edition by W. H. Inmon, Claudia Imhoff, Ryan Sousa
Building the Data Warehouse, 2nd Edition by W. H. Inmon
With OLTP, the tables are designed to facilitate fast inserting, updating and deleting rows of information with each logical unit of work. The database design is highly normalized. Usually and at least to 3NF. Each logical unit of work in an online application will have a relatively small scope with regard to the number of tables that are referenced and/or updated. Also the online application itself handles the majority of the work for joining data to facilitate the screen functions. This means the user doesn't have to worry about traversing across large data relationship paths. A heavy dose of lookup/reference tables and much focus on referential integrity between foreign keys. The physical design of the database needs to take into considerations the need for inserting rows when deciding on physical space settings. A good book for getting a solid base understanding of modeling for OLTP is The Data Modeling Handbook: A Best-Practice Approach to Building Quality Data Models by Michael C. Reingruber, William W. Gregory.
Example: Let's say we have a purchase oder management system. We need to be able to take orders for our customers, and we need to be able to sell many items on each order. We need to capture the store that sold the item, the customer that bought the item (and where we need to ship things and where to bill) and we need to make sure that we pull from the valid store_items to get the correct item number, description and price. Our OLTP data model will contain a CUSTOMER_MASTER, A CUSTOMER_ADDRESS_MASTER, A STORE_MASTER, AN ITEM_MASTER, AN ITEM_PRICE_MASTER, A PURCHASE_ORDER_MASTER AND A PURCHASE_ORDER_LINE_ITEM table. Then we might have a series of M:M relationships for example. An ITEM might have a different price for specific time periods for specific stores.
With OLAP, the tables are designed to facilitate easy access to information. Today's OLAP tools make the job of developing a query very easy. However, you still want to minimize the extensiveness of the relational model in an OLAP application. Users don't have the wills and means to learn how to work through a complex maze of table relationships. So you'll design your tables with a high degree of denormalization. The most prevalent design scheme for OLAP is the Star-Schema, popularized by Ralph Kimball. The star schema has a FACT table that contains the elements of data that are used arithmatically (counting, summing, averaging, etc.) The FACT Table is surrounded by lookup tables called Dimensions. Each Dimension table provides a reference to those things that you want to analyze by. A good book to understand how to design OLAP solutions is The Data Warehouse Toolkit: Practical Techniques for Building Dimensional Data Warehouses by Ralph Kimball.
Example: let's say we want to see some key measures about purchases. We want to know how many items and the sales amount that are purchased by what kind of customer across which stores. The FACT table will contain a column for Qty-purchased and Purchase Amount. The DIMENSION tables will include the ITEM_DESC (contains the item_id & Description), the CUSTOMER_TYPE, the STORE (Store_id & store name), and TIME (contains calendar information such as the date, the month_end_date, quarter_end_date, day_of_week, etc).
Database Fundamentals > Data Warehousing and Business Intelligence with Mike Lampa
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hope it helps...

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veryConfused wrote:
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Difference Between Aggregates and Compression

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