![]() Heavy limitations in the scope of metadata, only fields and elements already present in the data dictionary.Metadata with data, directly in the database.So to summarize, the stack looks like this: This can be a feature built into a database GUI or a standalone tool. Then such a data dictionary can be shared with database documentation generator that generates HTML, PDF or another format for easy access. Comments can be edited with many database management tools that are available for all databases. Some teams choose to store their metadata in those structures. Most DBMSs also have the ability to annotate data dictionary elements (called comments, descriptions or extended properties). ![]() All database engines (DBMS) have a so-called active data dictionary - an inventory of their data structures. DBMS + GUI tool (+ Generator)Īnother very popular approach is to make use of DBMS built-in data dictionary. Population, and especially maintenance, can be a nightmareĭata Dictionary in Excel spreadsheet 2.But the maintenance is the hard part - making sure it's up to date with the source can be a laborious task. Those are generic tools for creating and collaborating on documents that most people are familiar with.Ĭreating a data dictionary can be as easy as extracting a list of columns from a database using a query and pasting the results into a spreadsheet for people to fill in the details. I think the most obvious tool, perhaps good for a proof of concept, is a spreadsheet software or word processor (preferably the former). In this article, I will present you with different types of tools that you can use to build and share such an inventory. A data dictionary is a definition of tables/files and columns/fields in a data set (database, data warehouse or data lake). Now, when accessing company data held in databases is becoming critical, organizations are looking for tools that will allow them to build and share data dictionary of their data sources.
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