SAP BW Architecture

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All leading analysts agree: enterprise-scale Business Intelligence needs an enterprise-class architecture, one that enables a quick and simple implementation, one that meets today's needs and tomorrow's desires, one that allows for enterprise-wide deployment to dozens, hundreds or even thousands of users.

These are precisely the benefits element61 can provide. Our experienced SAP BW architects can guide you on your journey towards a successful SAP Enterprise Data Warehouse. It is proven best practice to build a Data Warehouse as a stack of layers. Each of the layers serves a well defined purpose, and this purpose is guarded by a set of clear and relatively simple rules.

In the element61 architecture guidelines that were developed and fine-tuned over the course of many years and projects, the following conceptual layers can be distinguished within the architecture of a single SAP BW Data Warehouse environment:

  1. Sources Layer
  2. Data Warehouse Layer
  3. Data Delivery Layer.

For the Sources Layer the following principles apply:

  • Only Sources bring data to the Data Warehouse
  • Data is sourced to meet immediate and anticipated future business needs
  • Data is extracted from the source only once
  • Data in the Data Warehouse is auditable against the Source
  • A Source can be any System
  • Data should be sourced to the Data Warehouse at a granular level
  • Data is extracted as is' from the source
  • Data is not pre-aggregated before entering the Data Warehouse

The Data Warehouse Layer is the part of the BW architecture where data is gathered and kept to serve any reporting and analysis needs of the organization. The Data Warehouse acts' as the memory of the organization and is often also referred to as the single version of the truth'. In the Data Warehouse the integration efforts are combined by storing all required data against a single agreed business model, thus resulting in a version of the truth that everyone agrees upon.

Within the Data Warehouse Layer three sub-layers are recognized:

  • Data Acquisition Layer
  • Transformation & Integration Layer
  • Data Container Layer

The Data Acquisition sub-layer captures source data in its most raw' form. This data is stored as long as it is required for transformation and integration purposes, and is archived thereafter.

The Transformation & Integration sub-layer bridges the gap between the Data Acquisition Layer, containing data against source keys, and the Data Container Layer, containing relevant business data against business keys.

The Data Container sub-layer stores data a (granular) format meaningful to the business. It contains common business flavored' datasets that represent a Single version of the truth'.

The Data Delivery Layer has a different function than the Data Warehouse Layer. Where the primary function of the Data Warehouse is to maintain the single version of the truth for any purpose, the Data Delivery Layer must serve the data to different user groups or business processes, often via different mechanisms (on-line and off-line reports, BI applications, web-access, etc.), and thus needs to be set up accordingly.

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