Data Warehousing - Metadata Concepts
What is Metadata?
Metadata is simply defined as data about data. The data that is used to represent other data is known as metadata. 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.
Note − In a data warehouse, we create metadata for the data names and definitions of a given data warehouse. Along with this metadata, additional metadata is also created for time-stamping any extracted data, the source of extracted data.
Categories of Metadata
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.
Role of Metadata
Metadata has a very important role in a data warehouse. The role of metadata in a warehouse is different from the warehouse data, yet it plays an important role. The various roles of metadata are explained below.
Metadata acts as a directory.
This directory helps the decision support system to locate the contents of the data warehouse.
Metadata helps in decision support system for mapping of data when data is transformed from operational environment to data warehouse environment.
Metadata helps in summarization between current detailed data and highly summarized data.
Metadata also helps in summarization between lightly detailed data and highly summarized data.
Metadata is used for query tools.
Metadata is used in extraction and cleansing tools.
Metadata is used in reporting tools.
Metadata is used in transformation tools.
Metadata plays an important role in loading functions.
The following diagram shows the roles of metadata.
Metadata Repository
Metadata repository is an integral part of a data warehouse system. It has the following metadata −
Definition of data warehouse − It includes the description of structure of data warehouse. The description is defined by schema, view, hierarchies, derived data definitions, and data mart locations and contents.
Business metadata − It contains has the data ownership information, business definition, and changing policies.
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.
Data for mapping from operational environment to data warehouse − It includes the source databases and their contents, data extraction, data partition cleaning,
transformation rules, data refresh and purging rules.
Algorithms for summarization − It includes dimension algorithms, data on granularity, aggregation, summarizing, etc.
Challenges for Metadata Management
The importance of metadata can not be overstated. Metadata helps in driving the accuracy of reports, validates data transformation, and ensures the accuracy of calculations. Metadata also enforces the definition of business terms to business end-users. With all these uses of metadata, it also has its challenges. Some of the challenges are discussed below.
Metadata in a big organization is scattered across the organization. This metadata is spread in spreadsheets, databases, and applications.
Metadata could be present in text files or multimedia files. To use this data for information management solutions, it has to be correctly defined.
There are no industry-wide accepted standards. Data management solution vendors have narrow focus.
There are no easy and accepted methods of passing metadata.
