Data is the lifeblood of any business, and data modelling is critical to understanding that data and turning it into insights that inform business decisions. The right data modelling tool can help simplify the process of designing, creating and maintaining complex data models.
Data models are used to document, store, and retrieve data from databases. Data modelling tools are used to create and maintain data models. Data modelling tools allow you to show how data structures that support business operations will be constructed, how data will be arranged, and what links we want to make between them.
Several different data modelling tools are available on the market, ranging from simple visualisation tools to more complex enterprise-grade solutions. In this article, we'll take a look at 11 of the best data modelling tools and other basic information to help you to make an informed decision about which tool is right for your needs.
What are Data Modeling Tools?
Data modelling is the process of designing, creating, documenting, and analyzing data models. Data modelling is the first step in the software engineering process. Its objective is to produce a data model that describes the data within an organization and how they are related.
Data modelling tools are software applications that aid in the creation, modification, and reverse engineering of data models. They are used to create, view, and edit data models. Some data modelling tools also have the ability to generate code from data models or to reverse engineer code into data models.
The Need for Data Modeling Tools:
As the amount of data being collected and stored by organizations continues to grow, so does the need for effective ways to manage that data. Data modelling tools can help with this by providing a way to visualize the data and see how it is related.
They can also be used to generate code or reverse engineer code into data models, which can make it easier to manage the data. There are a wide variety of data modelling tools available, ranging from simple to complex.
Some are designed for specific purposes, such as reverse engineering or code generation, while others are more general purpose. Choosing the right tool depends on the needs of the organization and the type of data being managed.
Types of Data Models:
There are several different types of data models
- Conceptual data model
- Logical data model
- Physical data model
Conceptual Data Model:
A conceptual data model is an abstract, high-level representation of the relationships between different concepts in a system. It is used to help developers better understand the business domain, identify potential areas of improvement, and capture the essential aspects of the domain in a concise and convenient form.
The model is also useful for communicating domain knowledge to non-technical stakeholders such as executives or customers. A conceptual data model typically contains entities, attributes, and relationships. Entities are the things that exist in the system being modelled, such as products, customers, or orders.
Attributes are the characteristics of entities, such as name, price, or quantity. Relationships are the connections between entities, such as "customer places order" or "product is part of the order." Conceptual data models are typically created using diagrams and notation schemes such as the Entity Relationship Model (ERM).
Logical Data Model:
The logical data model is a data modelling technique that takes into account the rules and resource limits when aggregating implementation details. This model can be used to define attributes that will constitute the key to the structure.
Companies that are involved in data warehousing often use this technique to help consolidate and segregate data for analytics purposes. The logical data model is created by data architects and can be used to convert it into a database.
A logical data model is a technology-independent way of organizing information using structures like relational tables and columns, object-oriented classes, or XML tags. In contrast, a conceptual data model only describes the meaning behind the organization without going into specifics about how that meaning is represented.
Physical Data Model:
The physical data model is a data modelling technique that takes into account the actual implementation details of a system. This model can be used to define the physical structure of the data, such as how it is organized on disk or in memory.
It can also be used to define performance characteristics, such as indexing and partitioning strategies. The physical data model is typically created by database administrators and can be used to generate SQL code for creating the database.
A physical data model is a technology-specific way of organizing information. In contrast, a logical data model defines the relationships between different concepts without going into specifics about how those concepts are represented.
Factors to Consider while Selecting the Data Modeling Tools
With the increasing complexity of Data Modeling Tools, it might be difficult to find the right one for you. That's why it's crucial to consider a few key factors that align with your specific use case.
A project's requirements always differ as it becomes more fleshed out. Make sure to use a tool that will enable you to accommodate those changes. When determining which Data Modeling Tool to choose, take into account the size and intended usage of the data model. With that in mind, select a tool that meets the current needs while still having room for growth.
Usage and Needs:
The most crucial step in choosing a Data Modeling Tool is to look at your specific business needs. Different tools focus on varied aspects of Data Modeling, so it's key to have an idea of what you require before beginning your search.
A project that has moderate tasks would be best suited for a tool with modelling capabilities right in the database, however, if data modelling needs to support corporate expansion, then this same technology will not suffice.
Don't forget to check if the software you're interested in has an active user community where people are available to answer any questions you might have. It's always a good idea to make sure your tool of choice won't leave you high and dry without anyone to turn to for help.
Once you understand the business needs, compare various Data Modeling Tools to see which one is the best fit. Consider things like UTM or use case modelling support, as well as whether multiple users can access the tool at once.
Also, check if it supports conceptual, logical, and physical data modelling. Make a list of features for each Tool and compare them side-by-side to make your decision easier.
Some data modelling tools use a generic format, while others use a proprietary format. Use caution when choosing your tool to make sure that the output will work well with your database or other existing technology.
Best Data Modeling Tools in the Market
Popular data modelling tools abound for various database platforms but finding one that perfectly fits your needs can be tough. Below is a list of some of the most popular data modelling tools:
Boltic is an easy-to-use platform that helps you manage your data more effectively. Our platform supports multiple databases, including CSV, JSON, XML, and more. You can use Boltic to generate reports, create data visualizations, and track changes to your database over time.
Additionally, Boltic is affordable compared to other data management tools on the market. Consequently, it is a popular choice for all businesses and startups that want to get the most out of their data without breaking the bank.
Draw.io is a free, intuitive, browser-based data modeling tool that is easy to use and ideal for quickly creating high-level data models. It has the ability to generate code from data models or reverse engineer code into data models.
It features drag-and-drop entity shapes and connectors, making it simple to create complex models. An SQL plugin is also available, which automatically generates entity shapes and attributes from SQL code.
Draw.io is helpful for designing finance models and other highly structured data sets. However, it may not be as useful for event-based data sets that are more unstructured.
3) MYSQL WORKBENCH
MySQL Workbench is a great tool for data modeling. It is easy to use and has a reputation for being intuitive. Moreover, it is free, open-source, and cross-platform. Companies use MySQL Workbench for structured data alongside the MicroStrategy analytics platform.
It is very helpful to develop a model in Workbench and then overlay it with an extensive logical model for reporting purposes. Additionally, team can regularly schedule an exercise where they would recreate the full model with Workbench and then examine how it fits into the environment.
Data catalogs are becoming increasingly important in the world of data, and data modeling tools are starting to reflect that. Amundsen is a data modeling tool that uses a graph to represent metadata entities, making it easy to extend and adapt as new entities are introduced.
All of the entities in the Amundsen model are connected via edges that represent the relationships between them, making it a powerful tool for understanding complex data sets.
5) SQUIRREL SQL CLIENT
The free, open-source graphical tool SQuirreL is supported by most major relational databases and provides features such as the ability to see database structures and entity-relationship diagrams.
SQuirreL may be appealing to new businesses as it does not require ODBC API for older data environments. However, keep in mind that no single data-modeling tool will suffice for all use cases. Agility, consistency, and data integrity are key considerations when working with data models.
6) ORACLE SQL DEVELOPER DATA MODELER
SQL Developer Data Modeler is a free data modeling tool that allows users to create relational, physical, and multidimensional models. It was developed by Oracle Corporation and was first released in 2009.
The data modeler has had over 20 iterations, and releases and future enhancements plan to include compatibility for MySQL, TimesTen, Sybase, and SQL Server 8. SQL Developer Data Modeler is used to forward and reverse engineer data. It is a powerful tool that makes data modeling easier and more efficient.
As an open-source tool, Metabase offers users a graphical interface with some helpful analytics visualizations. It is designed to be user-friendly and easy to deploy and is ideal for cross-team data access and analysis.
Ng found Metabase easy to use and configure and was able to get it up and running quickly. He was impressed with the tool's ability to let users join tables and define entity relationships. Overall, Metabase is a powerful yet user-friendly tool that is perfect for data analysis.
DataGrip is popular among database administrators because it allows users to add, remove, edit and clone data rows and use text search to easily find the information displayed.
Context-sensitive, schema-aware code completion and auto-generating code for changing objects like tables and columns based on UI are some of the features that make writing SQL code easier.
DataGrip also has a query console that supports schema switching and provides a historical record of your activity and work. This makes it easy to keep track of your progress and see what you've done in the past.
Lucidchart is a powerful diagram builder that offers more complex flows and stronger data protection than its competitors. It's also part of DoorDash's toolkit, which means it's been battle-tested by one of the hottest startups around.
Lucidchart links to SQL, which makes it easy to pipe your diagrams directly into your database management system. Whether you're working with PostgreSQL or another database, Lucidchart is an incredibly useful tool.
10) ERWIN DATA MODELER
Erwin Data Modeler is a tool that helps organizations manage their data more effectively. It supports multiple databases, including MySQL, PostgreSQL, and Teradata.
Additionally, it can generate DDL statements and help identify differences between different versions of a schema. This makes it an invaluable tool for keeping track of changes to a database over time.
Erwin Data Modeler is also relatively affordable compared to other data management tools on the market. Consequently, it is a popular choice for organizations that want to get the most out of their data without breaking the bank.
ER/Studio is a powerful data modeling tool that has been on the market for many years. It is designed for enterprise users and offers a wide range of features, such as reverse engineering, forward engineering, model comparisons, and documentation generation.
Additionally, ER/Studio provides an intuitive interface that makes data modeling easy for even the most novice users. As a result, it is one of the most popular data modeling tools on the market.
Key Benefits of Data Modeling Tools
Data Modeling Tools have many advantages, some of which are mentioned below. By using these tools, you can take a complicated software process and turn it into an easy-to-understand visual.
- Helps users understand data dependencies.
- Reduces the time it takes to develop a database.
- It Makes it easy to make changes to a database.
- Facilitates communication between different teams working on a project.
- Helps identify potential problems early on in the development process.
- Saves the organization's money by reducing the need for expensive database development tools.
- It improves the quality of data by allowing users to spot errors and inconsistencies early on.
- Allows organizations to make better use of their data.
- Helps developers create more reliable databases.
- Reduces the risk of data loss by making it easy to track changes to a database over time.
- Data Modeling Tools make it easier and faster to design a database at the conceptual, logical, and physical levels.
Data modeling is a critical part of any data management strategy. The right data model can make it easier to store, query, and analyze data. With boltic, you can easily join multiple data sources and create entity relationships.
Additionally, Boltic supports schema switching and provides a historical record of your activity. This makes it easy to keep track of your progress and see what you've done in the past. Overall, Boltic is a powerful yet user-friendly tool that is perfect for data analysis.