DW - Data Warehousing & Modeling

SAP BW DataStore object (DSO)

A BW DataStore Object is the object which represents a flat physical table.

In a dataflow, it is typically used as an Enterprise Data Warehouse layer, to store (historical) transactional data before it is transferred to the reporting layer. It is also the object where the delta is processed and SID’s are generated.
 

For reporting it should only be used to list transactional data on the lowest level of detail.

SAP BW Extractor

An extractor is the program in SAP ERP which can be activated to prepare and capture the data through an extract structure for transfer to BW. The extractor can be the result of a standard DataSource or a custom built DataSource. It can describe a full load or a delta load process of different types.

SAP BW InfoObject

This is the lowest level of metadata existing in the SAP Business Warehouse.

There are two main types of InfoObjects: characteristics – representing dimensions for analysis - and key figures – representing the numeric data to be analyzed. The metadata definition consists of technical properties (Data Type, length, conversion routine, …) and descriptive properties (text and attribute Master Data, hierarchies, navigation properties, …).

SAP BW InfoProvider

There
are different objects available in SAP BW for reporting, which can all be
described as InfoProviders because they are holding and providing information
to the end user of SAP Business Intelligence.


Examples of InfoProviders are:

  • MultiProvider,
  • InfoCube,
  • DataStore Object,
  • InfoObject,
  • InfoSet, …

SAP BW InfoSource

The SAP BW InfoSource is a concept which used to be mandatory in a 3.x dataflow.

It is a view that had to be created between the DataSource and InfoProvider, to map the SAP fields to InfoObjects.

In the newer versions it is not a mandatory object anymore. However the object is still available to use and will be found in different Business Content data flows.

SAP BW Multiprovider

The MultiProvider does not represent physical tables, but is defined as a view on other InfoProviders like InfoCubes, DataStore Objects, InfoObjects, …

It is also represented as a dimensional model, and the InfoObjects inside this model are mapped to the physical InfoProviders. It is a best practice to always build your queries on a MultiProvider, for reasons of extensibility and re-usability.

SAP BW Transformation

The transformation is the program which maps one InfoProvider to another. It is the core of the ETL in BW, as all data transformations are defined here. InfoObjects are mapped here, and transformation rules can be defined, if necessary with ABAP programming. In order to launch a transformation, so that the data is transformed and loaded from one InfoProvider to another, a Data Transfer Process (DTP) needs to be launched which is linked to the transformation.

Microsoft SQL Server Database Engine Tuning Advisor

The Microsoft SQL Server Database Engine Tuning Advisor helps you to optimize database configuration by identifying an optimal set of indexes, indexed views and partitions without requiring an expert understanding of the database structure. It advises you by analyzing a certain workload by using trace files, trace tables or T-SQL scripts as input.

The Database Engine Tuning Advisor will then give you certain recommendations to add, remove or modify certain settings in your Microsoft SQL Server database.

Snowflake schemas

Two "dimensional modeling” techniques are generally used for modeling the data structures within a data warehouse context.

These are the "star schema” and "snowflake schema” techniques. Both find their roots within the Kimball dimensional modeling techniques.

Properties of the snowflake schemas :

SAP BW analysis process

These are separate processes which can be set up to perform specific analysis in batch mode, next to the ETL process. These processes can incorporate data mining concepts like regression analysis, prediction with decision tree, …
Typical usage is the ability to use queries in APD processes, perform joins between multiple datasets, perform complex calculations, …

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