Jun 21, · Data Dictionary: a how to and best practices. A data dictionary is a list of key terms and metrics with definitions, a business glossary. While it is sounds simple, almost trivial, its ability to Estimated Reading Time: 7 mins For a spreadsheet. Submit a spreadsheet .XLS blogger.com) with one tab for introductory information, and separate data dictionary tabs that correspond to each existing tab in your dataset. For example, if your dataset consists of three tabs, your data dictionary will have four tabs: the first for introductory, background information, and three more to correspond to the three tabs of data This article is licensed under CC0 for maximum reuse.. A data dictionary is critical to making your research more reproducible because it allows others to understand your data. The purpose of a data dictionary is to explain what all the variable names and values in your spreadsheet really mean
What is a SQL Server Data Dictionary and why would I want to create one?
A SQL Server database can be thought of, in and of itself, as a data dictionary. It is self-describing, in that objects can be scripted into Data Definition Language aka DDL scripts to view all attributes, quickly and easily. The self-describing nature of a database can be enhanced with Extended propertieswhich allow user defined descriptions to be attributed to objects at various levels in the database.
Documentation can be referred as a single instance, or snapshot, the database rendered in an easily readable, user friendly format like HTML or compiled HTML e, how to write database dictionary. chm that any user can view, even without database experience. Documentation sample of the Client table. Click here to learn more about SQL Server database documentation.
A database, itself, and database documentation are great, how to write database dictionary, but they are one dimensional snapshots in time.
Neither can provide the context of changes or history. Version or source control provides a solution for that in that all objects can be versioned over time, allowing a user to browse any version of an object or even the entire database in time. Additionally, version control allows users to revert to any previous object version or the entire database itself.
Click here to learn more about SQL Server source control. But since a database dictionary exists, itself, in a database, unlike version how to write database dictionary, it lends itself to being easily queried and reported against.
A simple data dictionary is a repository of information about the database, usually in a database itself, that stores object information and attributes, versioned over time.
Dumb dictionaries are repository of schema snapshots, regardless of what if any data changed. Such dumb dictionaries can be created by simply scripting the schema to a table nightly. This allows users querying the data to mine real information, knowing that each new version of an object, represents an actual change. In this way, a version history of an object would show only versions that actually changed, making this feature as powerful as version history in source control.
Knowing that each object record reflected an actual change, we can layer on lots of value added reports that instantly tell the transactional history of the database e. The primary purpose of a data dictionary is to be an auditing archive of changes that is easy to query and report on.
Large amounts of data can be stored efficiently leveraging the power of a RDBMS. Data can be easily filtered, extracted, sorted and grouped into meaningful reports. A data dictionary provides information that can easily be converted into other views. For example, you could query your data dictionary to see the full history of changes for a particular object, how to write database dictionary.
A data dictionary can be used for version control and auditing purposes, even without using a source control repository, clients or having developers even know changes were being logged. Data dictionaries provide a great medium for transactional and aggregate reporting.
Since the data dictionary is a repository of all changes, it can be used to create reports to track the count of transactions over time, by user, by object and produce a series of reports, that provide great insight into the transactional how to write database dictionary of a database over time, like shown below:. Stay tuned! ALL RIGHTS RESERVED. GDPR Terms of Use How to write database dictionary. Author Recent Posts.
Alen Gubicak. Support Sales Engineer at ApexSQL. Alen is a Media designer with a passion for IT and music. He plays bass guitar and open world video games in his spare time. He loves animals and recently started paragliding. Alen is currently working for ApexSQL LLC as a Software Sales Engineer where he specializes in SQL and BI documentation and database Analysis.
View all posts by Alen Gubicak. Latest posts by Alen Gubicak see all. What is a SQL Server Data Dictionary and why would I want to create one? Related posts: How to create a new Git repository on Bitbucket and review committed objects How to create charts from SQL Server data using PowerShell SQL Server performance monitoring with Data Collector — Part 3 — reading the reports Using Master Data Services in SQL Server to quickly create a GUI that may be maintained by the end user Revision history of an object change how to write database dictionary a SQL database using Team Foundation Server.
Oracle Tutorial - Oracle SQL Data Dictionary
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This article is licensed under CC0 for maximum reuse.. A data dictionary is critical to making your research more reproducible because it allows others to understand your data. The purpose of a data dictionary is to explain what all the variable names and values in your spreadsheet really mean Jun 21, · Data Dictionary: a how to and best practices. A data dictionary is a list of key terms and metrics with definitions, a business glossary. While it is sounds simple, almost trivial, its ability to Estimated Reading Time: 7 mins For a spreadsheet. Submit a spreadsheet .XLS blogger.com) with one tab for introductory information, and separate data dictionary tabs that correspond to each existing tab in your dataset. For example, if your dataset consists of three tabs, your data dictionary will have four tabs: the first for introductory, background information, and three more to correspond to the three tabs of data
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