SQL Server
Oracle
MySQL
PostgreSQL
Amazon Redshift
Azure SQL Database
DBT
Google Big Query
IBM DB2
MariaDB
SAP HANA
Snowflake
SQLite
Teradata
Vertica
Data dictionary tools for MongoDB
List of tools that enable design and building of data dictionaries .
Data Dictionary is a set of important information about data used within an organization (metadata). This information includes names, definitions, and attributes about data, owners, and creators of assets. Data Dictionary tools provide insights into meaning and purposes of data elements. They add useful aliases about the scope and characteristics of data elements, as well as the rules for their usage and application.
Dataedo
Dataedo allows you to connect and scan metadata from multiple sources and build data dictionary automatically in a couple of minutes.
| Desktop/Cloud: | Desktop |
|---|---|
| ER Diagram: |
|
| Export: | HTML,MS Excel,PDF |
| Metadata stored in: | Documentation repository/file |
| Commercial: | Commercial |
| Free edition: |
|
| Notable features: | ER diagrams, metadata repository, schema change tracking, organizing with modules, documenting missing FKs, custom fields, description suggestions, documentation progress tracking, rich text with images |
| Runs on: (for desktop): | Mac OS,Windows |
Hackolade
Hackolade is a tool for Agile visual data modeling for JSON and NoSQL databases. It provides graphic visualization of complex data structures using Entity-Relationship diagrams to represent denormalized data in a user-friendly way. Besides that, it provides the functionality of designing one data dictionary of objects and using it in different models.
| Desktop/Cloud: | Desktop |
|---|---|
| ER Diagram: |
|
| Export: | HTML,PDF |
| Metadata stored in: | Program metadata |
| Commercial: | Commercial |
| Free edition: |
|
| Notable features: | Graphical visualization, forward and reverse engineering, data governance, data model documentation |
| Runs on: (for desktop): | Linux,Mac OS,Windows |
DbSchema
DbSchema facilitates to design, document and manage SQL and NoSQL databases. It is an intuitive designer for complex databases. It allows editing tables or columns directly in the layout, by double-clicking them.
| Desktop/Cloud: | Desktop |
|---|---|
| ER Diagram: |
|
| Export: | HTML,PDF |
| Metadata stored in: | Database metadata |
| Commercial: | Commercial |
| Free edition: |
|
| Notable features: | Entity relationship diagram, Reverse engineer schema from database, Relational data browse, SQL editor |
| Runs on: (for desktop): | Linux,Mac OS,Windows |
GenMyModel
With GenMyModel’s data modeling capabilities, data architects not only keep glossaries and data models under control, but they can design and reverse engineer data models and link them to other IT and business models, such as UML, Archimate and BPMN.The resulting data dictionary stores metadata such as table, column and field descriptions,
in a format that is independent of the underlying database system so generation can be generic.
| Desktop/Cloud: | Cloud |
|---|---|
| ER Diagram: |
|
| Export: | HTML,MS Word,Online HTML,PDF,Plain text,XML |
| Metadata stored in: | Online repository |
| Commercial: | Commercial |
| Free edition: |
|
| Notable features: | - |
| Runs on: (for desktop): | - |
ER/Studio
ER/Studio Data Architect helps to easily reverse- and forward-engineer, compare and merge, and visually document data assets across multiple platforms and data sources. The Data Dictionary is a feature of ER/Studio that allows the sharing of many objects including domains, defaults, rules, and attachments. Using the Data Dictionary you can enforce standards, promote reuse, and build a common framework across all models.
| Desktop/Cloud: | Desktop |
|---|---|
| ER Diagram: |
|
| Export: | HTML |
| Metadata stored in: | Documentation repository/file |
| Commercial: | Commercial |
| Free edition: |
|
| Notable features: | Data modeler, Entity relation diagram, Model sharing |
| Runs on: (for desktop): | Windows |
Tree Schema
The Tree Schema data dictionary provides a single place to define all of the key terms and lingo that drive your business. The Tree Schema dictionary is automatically synced with the tags within Tree Schema, making it easy to define the labels that are important to your business and leverage them to tag your data assets.
| Desktop/Cloud: | Cloud |
|---|---|
| ER Diagram: |
|
| Export: | - |
| Metadata stored in: | - |
| Commercial: | Commercial |
| Free edition: |
|
| Notable features: | Automatically syncs tags with business glossay |
| Runs on: (for desktop): | - |
Alation Data Catalog
Alation data dictionary defines and describes technical data terms. Data terms could be database schemas, tables, or columns. Once connected to data sources, Alation automatically indexes data and populates catalog pages. For example, a column catalog page shows the technical column name, a business title name, the data type, and popularity. Additional context can be added to the data dictionary, for shared understanding across the organization.
| Desktop/Cloud: | Cloud |
|---|---|
| ER Diagram: |
|
| Export: | MS Excel |
| Metadata stored in: | - |
| Commercial: | Commercial |
| Free edition: |
|
| Notable features: | ML auto-suggested business glossary terms |
| Runs on: (for desktop): | - |
Key functionality of Data Dictionary tools is to give users the ability to document data. Moreover, very important is the possibility to create a collection of multiple repositories, based on different system engines. For a better understanding of the data, some tools allow visualization of the data structure using ERD (Entity-Relationship Diagrams).
From the organization's point of view, a community module within a data dictionary tool proves to be useful. It facilitates the proper information flow, as well as provides sharing opinions on specific objects among the members of an organization.
Nowadays, data discovery and understanding becomes crucial for proper organization performance. There are many benefits to using Data Dictionaries, such as:
• helps avoid data inconsistencies problems,
• it allows introducing unified nomenclature used in the project,
• Make data searchable, and understandable,
• Create a single source of truth about the data from different repositories,
The prepared list includes simple, open-source data dictionaries as well as more advanced software.