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It can be understood like this:
Metadata describes data. It is “data about data”. It contains information about how, when, by whom, and in what format certain data is collected. Understanding the information stored in data warehouses and xml-based web applications is critical. A data dictionary is a file consisting of the basic definitions of a database. It contains a list of files available in the database, the number of records in each file, and information about fields. The data dictionary is the repository where all information is stored. Metadata is data about data. Metadata is data that defines other data. So a data dictionary can be metadata that describes some information about a database.
Let’s take a closer look at the main content of metadata and data dictionaries.
Metadata refers to “data about data”. Although the “meta” prefix means “after” or “after,” in epistemology it is used to mean “about.” Metadata is defined as data that provides information about one or more aspects of data; it is used to summarize basic information about data in order to more easily track and process specific data. Some examples include:
• Time and date of creation
• Creator or author of the data
• Where data is created on a computer network
• The process used to create the data
For example, a digital image might contain metadata describing the image’s size, color depth, resolution, creation time, shutter speed, and other data. Metadata for a text document might contain information such as: how long the document is, who the author is, the When it was written and a short summary of the document. Metadata in a web page can also contain a description of the page’s content, as well as keywords linking to the content. These links are often called “meta tags” and were used until the late 1990s as the main factor in determining the search order of web pages. In the late 1990s, meta tags were largely misused to trick search engines into thinking that some sites were in the Searches have more relevance than they actually do.
Metadata can be stored and managed in a database, often called a metadata registry or metadata repository. However, metadata may not be identifiable by observation alone without context and reference points. Example: By itself, a database containing several numbers, all 13 digits could be the result of a calculation or a list of numbers inserted into an equation – without any other context, the numbers themselves could be considered data. However, given the context that this database is a bibliographic log, these 13-digit numbers can now be identified as ISBNs—information referring to the book, but not itself. The term “metadata” was coined by Philip Bagley in 1968 in his book “Extensions of the Concept of Programming Languages”, and apparently he used the term in the “traditional” sense of ISO 11179, i.e. “structure metadata”. “data about a data container”; as opposed to another meaning “content about a single instance of data content” or meta-content, the type of data typically found in library catalogs. Since then, the term has been widely adopted in fields such as information management, information science, information technology, library management, and GIS. Within these fields, the term metadata is defined as “data about data.” While this is a generally accepted definition, different disciplines have adopted their own more specific interpretations and uses of the term.
While metadata applications are diverse and cover a wide variety of fields, there are specialized, widely accepted models for specifying types of metadata. Bretherton and Singley (1994) distinguished two distinct categories: structural/control metadata and bootstrap metadata Structural metadata describes the structure of database objects such as tables, columns, keys, and indexes. Guide metadata helps people find specific items, usually expressed in natural language as a set of keywords. According to Ralph Kimball, metadata can be divided into 3 categories: technical metadata, business metadata and operational metadata.
While metadata applications are diverse and cover a wide variety of fields, there are specialized, widely accepted models for specifying types of metadata. Bretherton and Singley (1994) distinguished two distinct categories: structural/control metadata and bootstrap metadata Structural metadata describes the structure of database objects such as tables, columns, keys, and indexes. Guide metadata helps people find specific items, usually expressed in natural language as a set of keywords. According to Ralph Kimball, metadata can be divided into 3 categories: technical metadata (or internal metadata), business metadata (or external metadata), and process metadata.
NISO distinguishes 3 types of metadata: descriptive, structured and administrative. Descriptive metadata is often used for discovery and identification, as information for searching and locating objects, such as title, author, subject, keywords, and publisher. Structural metadata describes how the components of an object are organized. An example of structured metadata is how pages are ordered to form chapters of a book. Finally, administrative metadata provides information to help manage sources. Administrative metadata refers to technical information, such as the type of file, or when and how a file was created. The two subtypes of administrative metadata are rights management metadata and preservation metadata. Rights management metadata explains intellectual property, and preservation metadata contains information about preserving and preserving resources.
Statistical data repositories have their own requirements for metadata in order to describe not only the source and quality of the data, but also the statistical processes used to create the data, which is of particular importance to the statistical community in order to validate and improve statistical data production processes.
Another metadata type that is beginning to be developed is accessibility metadata. Accessibility metadata is not a new concept for libraries; however, advances in universal design have increased its popularity. Projects like Cloud4All and GPII have found that the lack of common terms and models to describe user needs and preferences, and the information to satisfy those needs, is a major gap in providing universal access solutions. These types of information are accessibility metadata.
metadata in images
Metadata for description
Metadata on the Web
metadata in the document
metadata in the database
The above are all examples of metadata. I hope you have a good understanding of what metadata is.
About the Data Dictionary
The data dictionary is the part of the database that holds information about the database and the data it stores called metadata so that we can manage the data. It can also be said that the data dictionary is one of the sources of metadata. The data dictionary does not contain information about the actual data in the database. Without a data dictionary, the database management system cannot access the data in the database. The database administrator handles the data dictionary, the user does not interact with it.
The data dictionary contains the following information:
• It contains the names of all tables and schemas present in the database.
• It contains detailed information about the tables present in the database, such as when the table was created, owner information about the table, etc.
• It contains constraint information about the table, such as primary key attributes.
• It also contains information about database views.
• It also contains physical information about the tables, such as about their storage, about their changes, etc.
The data dictionary is used to actually control database operations, data integrity and accuracy. Developers use metadata to develop programs, queries, controls, and programs to manage and manipulate data. Metadata is available as online system documentation to database administrators (DBAs), designers, and authorized users. This improves the control of the information system by the database administrator (DBA) and the understanding and use of the system by users.
Types of data dictionaries
There are two types of data dictionaries: active and passive.
Data dictionaries can be active or passive. Active data dictionaries (also known as integrated data dictionaries) are managed automatically by database management software. Consistent with the current structure and definition of the database. Most relational database management systems contain active data dictionaries that can be derived from their system catalogs.
When the database management system makes any changes to the database, the data dictionary is also updated. It’s called Active Data Dictionary. It can also be said that if the structure or anything else of the database changes, then the data dictionary for that database will also change. This is the task of the database management system.
A passive data dictionary (also known as a non-integrated data dictionary ) is one that is used for documentation purposes only. Data about fields, files, people, etc. in a data processing environment. Enter a dictionary and cross-reference it. Passive Dictionary is just a standalone application or form. It is managed by the users of the system and is modified when the database structure changes. Since this modification must be performed manually by the user, the data dictionary may not be kept in sync with the current structure of the database. However, a passive data dictionary can be maintained as a separate database. Thus, it allows developers to remain independent from using a particular relational database management system. It can be extended to include information about organizational data that is not computerized.
In a passive data dictionary, the content of the dictionary is not updated automatically, every change is made in the database by the database management system. Therefore, we have to manually update it from time to time. It is maintained separately from the database. Passive data dictionaries are not handled as easily as active data dictionaries. We must maintain it carefully so that the synchronization between the data dictionary and the database is not interrupted.
The Importance of Data Dictionary
A data dictionary is essential in a DBMS for the following reasons:
• The data dictionary provides the name of the data element, its description and the data structure in which it can be found.
• Data dictionaries are of great help in generating reports on where a data element is used in all programs that mention it.
• Given a keyword describing the name, data names can also be searched. For example, you might want to determine the name of a variable representing net salary. Entering keywords will generate a list of possible identifiers and their definitions. Keywords allow you to search the dictionary to find the correct identifier to use in your program.
Today, commercial data dictionary packages are available to facilitate the entry, editing, and use of data elements.
Data Dictionary Functions
Its functions are as follows:
• It defines the data objects for each user in the database. As we all know, we can’t remember all the tables, views, constraints, etc., so users can easily search them when any data definition language (DDL) triggers, and then the database searches the data dictionary. DBMS software update object.
• It provides us with reports on the data and resources the objects are using.
• It allows those users who have access to the database to view tables and views. Therefore, it controls access to the database.
Advantages of Data Dictionary
• Use a data dictionary so we can remove duplication in data definitions.
• Because it provides documentation. Therefore, it is a valuable reference for any organization.
• Help analysts simplify the structure to meet the requirements of the system data.
• It helps to improve communication between users and system analysts.
• Most database management systems include data dictionaries as standard features.
• Through this, new database administrators can easily understand the database of the system.
• Database administrators can easily track any problems in the database.
data table in database
data table in database
Data dictionary for the Customer_Age column
Relationship between metadata and data dictionary
Metadata is essentially information about data. Metadata contains information about when data was collected, how it was collected, and by whom. This helps enhance business intelligence and gives teams a better understanding of the data their company has. With automated metadata management, BI and analytics teams can immediately locate relevant data, identify the data’s point of origin, and create sound insights. By creating data about data, teams can also set processes and policies to ensure information can be easily accessed, shared, linked, integrated and analyzed. This ensures the data is relevant and accurate for all members of the company.
Metadata helps populate the data dictionary. In the data dictionary, BI teams can upload any data elements they have saved from different databases or descriptions. It is a file containing the basic definition of the database. The data dictionary is the primary tool that BI professionals use to organize all metadata. All the information related to the data present in the company Data Warehouse (DWH) is stored in the Data Dictionary. The data dictionary is used by the technical team and is the main place to refer to different data attributes, including constraints, data types, default values, lengths, conversion rules, and business definitions. By establishing coherent definitions that the entire company can understand, all teams can be on the same page. This helps maintain data validity and achieve consistency within the organization.
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