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Anjali Charde - 70
Paresh Patil - 35
Abhijith Panicker - 64
Kamlesh Agrawal - 22

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Introduction to Metadata in Warehouse...!

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Introduction to Metadata in Warehouse...!

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Metadata is data about the data or documentation about the information which is required by the users. In data warehousing, metadata is one of the essential aspects.

For example,
the index of a book serves as a metadata for the contents in the book. In other words, we can say that metadata is the summarized data that leads us to detailed data. In terms of data warehouse, we can define metadata as follows.

Metadata is the road-map to a data warehouse.

Metadata in a data warehouse defines the warehouse objects.

Metadata acts as a directory. This directory helps the decision support system to locate the contents of a data warehouse.

Metadata is used for building, maintaining, managing, and using the data warehouses. Metadata allow users access to help understand the content and find data.

Several examples of metadata are:

1. A library catalog may be considered metadata. The directory metadata consists of several predefined components representing specific attributes of a resource, and each item can have one or more values. These components could be the name of the author, the name of the document, the publisher's name, the publication date, and the methods to which it belongs.

2. The table of content and the index in a book may be treated metadata for the book.

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Metadata in a data warehouse fall into three major categories:

Operational Metadata

Extraction and Transformation Metadata

End-User Metadata

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Consistency of definitions:-
Metadata contains information about data that helps reconcile the difference in terminology such as "clients" and "customers," "revenue" and "sales," etc.

Clarity of relationships:-
Metadata helps resolve ambiguity and inconsistencies when determining the associations between entities stored throughout data environment.

Clarity of data lineage:-
Metadata contains information about the origins of a particular data set and can be granular enough to define information at the attribute level

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Metadata can be broadly categorized into three categories −

Business Metadata − It has the data ownership information, business definition, and changing policies.

Technical Metadata − It includes database system names, table and column names and sizes, data types and allowed values. Technical metadata also includes structural information such as primary and foreign key attributes and indices.

Operational Metadata − It includes currency of data and data lineage. Currency of data means whether the data is active, archived, or purged. Lineage of data means the history of data migrated and transformation applied on it.

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Once the metadata is created, you may face the below challenges while integrating and managing metadata in the system.

Bringing various formats of metadata into a standard format may need more effort if various tools are being used in the DW system, Because metadata can be stored across spreadsheets, applications (or) databasesBringing various formats of metadata into a standard format may need more effort if various tools are being used in the DW system, Because metadata can be stored across spreadsheets, applications (or) databases..

Metadata formats do not have any established industry-wide standards. With this lack of standardized process, it is tough to pass metadata through various levels of the DW system and tools.

Consistently maintaining various versions of historical metadata is a complex task.

Lack of clarity on true source of data

Lack of ability to manage data quality issues