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Execution-MiH ENCYCLOPEDIA →
Enterprise Intelligence →
SECTION - Metadata Management →
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CHAPTER - Metadata Architecture and Design
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Metadata Architecture Considerations are very similar to that of Data Warehouse. The difference is that DW is a Warehouse of data and metadata warehouse is the warehouse of Metadata. As you go through this chapter, you will find many parallels to the Data Warehouse architecture, and most of our examples will also be cued from Data Warehouse.
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Topics
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Metadata Architecture Design
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A metadata architecture and design needs to be well-integrated, Open, Robust, Automated and scalable.
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Metadata standards
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Metadata standards encompass metadata models, naming conventions, data standards within Metadata, metadata exchange standards and semantic layer. The concepts of metadata standards is similar to any business system.
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Metadata Extraction, Transformation and Loading
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Most of the ETL concepts of Metadata are same as that of a data warehouse. In this page, we will focus on providing examples and tips which are specific to Metadata.
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Metadata Architecture Scenarios
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A single all-enterprise physical metadata repository is found to be non-feasible. There are other architecture topologies which are more practical. Examples are Metadata warehouse, federated metadata and two way metadata.
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Metadata Repository Extraction Design
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Metadata extraction is the first stage in Metadata environment. It is equivalent to the extraction stage of a < href="http://www.ExecutionMiH.com/template/sections.php?section_id=D">data warehouse. We will be looking in detail at various sources and also the mechanism by which sourcing layer gets the data and puts it into the integration layer, which is equivalent to the Data Warehouse Transformation and loading in data warehouse. The extraction layer picks-up data from various sources. Examples are Software tools, end-users, documents, messaging and transactions, applications, web sites & e-commerce and 3rd parties.
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Metadata Repository Transformation Design
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Metadata Transformation is equivalent to the transformation stages of data warehouse architecture. This layer picks up the extracted metadata, and does the transformation. This includes making metadata consistent, integrated, standardized and clean.
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All Chapters in "Metadata Management." Section
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