Architecture of a Data Warehouse
There are many different definitions of a data warehouse. However, they all favor a layer-based architecture. The layers of a data warehouse on traditional databases mainly describe the various persistent storage layers of data across its history from the source to its availability for reports and analyses.
Traditionally the following persistent layers are available, which differ from the data due to their different services:
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(Peristent) Staging Area
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Data Warehouse
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(Architected) Data Marts
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Operational Data Store
Note: The layer architecture of the data warehouse is largely conceptual. In reality the boundaries between these layers are often fluid; individual data memory can play a role in two different layers. The technical implementation is always specific to the organization.
SAP BW, Edition for SAP HANA supports all these layers, but relies heavily on virtualizing services instead of delivering services by saving data anew. This means that whether and to which extent the individual layers still need to be characterized in SAP BW, Edition for SAP HANA differs markedly from the LSA for BW on traditional databases.
Staging Area
After it is extracted from source systems, data is transferred to the entry layer of the data warehouse, the staging area. In this layer, data is stored in the same form as in the source system. The way in which data is transferred from here to the next layer incorporates quality-assuring measures and the transformations and clean up required for a uniform, integrated view of the data.
Data Warehouse
The result of the first transformations and clean up is saved in the next layer, the data warehouse. This data warehouse layer offers integrated, granular, historic, stable data that has not yet been modified for a concrete usage and can therefore be seen as neutral. It acts as the basis for building consistent reporting structures and allows you to react to new requirements with flexibility.
Architected Data Marts
The data warehouse layer provides the most multidimensional analysis structures. These are also called architected data marts. This layer satisfies data analysis requirements. A data mart is not necessarily the same as summarization or aggregation; it also provides highly granular structures, but these focus on data analysis requirements alone, unlike the granular data in the data warehouse layer which is application neutral so as to ensure reusability.
The term "architected" refers to the fact that these data marts are not isolated applications but are based on a universally consistent data model. This means that master data can be reused in the form of Shared or Conformed Dimensions.
Operational Data Store
As well as strategic data analysis, a data warehouse also supports operative data analysis by means of the operational data store. Data can be updated to an operational data store, on a continual basis or in short intervals, and then read for operative analysis. You can also forward the data from the Operational Data Store layer to the data warehouse layer at set times.