The 10 Best Data Modeling Tools and Software to Consider in 2024

Data modeling tools, also called data modelers, have become an absolute necessity in the design of databases and data warehouses, so much so that there are hundreds of solutions to choose from. This is why this blog will take a deep dive into the best data modeling tools, software, and solutions to consider in 2024.

What Are Data Modeling Tools?

Data modeling tools are software solutions that provide a visual user interface (UI) for designing and implementing data structures. These tools empower data professionals to create conceptual, logical, and physical data models that define how data elements relate to one another and how they can be stored and accessed.

Why should businesses consider data modeling tools?

Data modeling, the blueprint for organizing and understanding organizational data, forms the foundation of efficient data management and analysis. Just like architects rely on building plans, data professionals leverage data models to map out the relationships between entities, attributes, and processes. However, building these models is easier said than done. And this is why businesses rely on data modeling tools—to streamline and accelerate the data modeling process.

Data modeling software facilitates a seamless transition from visual representations to detailed technical specifications, bridging the gap between business and tech users. Since visual representations typically provide an intuitive and accessible starting point, they allow business users to grasp complex data concepts without having to delve into technical intricacies. And as these representations evolve into detailed technical specifications, tech experts can precisely implement the envisioned data structures.

This way, both parties share a common understanding which leads to effective communication and alignment between business needs and technical implementations.

Categories Of Data Modeling Tools

While seemingly simple, data modeling tools encompass a diverse landscape as they cater to various business use cases. As such, data modeling software can be categorized into various types or classifications:

Comprehensive data modeling tools

As the name suggests, comprehensive data modeling tools are solutions catering to a wide range of business needs and use cases. These are typically enterprise-grade tools and solutions that provide a broad set of features and abilities. For example, besides supporting different data modeling techniques, such as dimensional modeling, data vault, etc., Astera Data Warehouse Builder offers users the ability to design, develop, and automate enterprise data warehouses from scratch within days.

Open-source data modeling tools

These are data modeling software with freely accessible source code. This means anyone can view, modify, and distribute the code, fostering transparency and encouraging community contributions. While they often lack dedicated support compared to paid options, open-source data modeling tools offer customization potential with community support and development. Examples include pgModeler for PostgreSQL and dbdiagram.io for general-purpose diagrams.

Free data modeling tools

For small businesses and individuals with budget-conscious projects, free data modeling tools offer a compelling choice. While these allow users to create basic database models without upfront costs, free data modeling tools fall short of features and extensive support available to their paid and open-source counterparts. Free tools like MySQL Workbench (limited edition) and Lucidchart (free plan) cater well to basic modeling needs, but for larger or more complex projects, considering paid options with deeper feature sets and expert support becomes critical.

The 10 Best Data Modeling Tools To Consider In 2024

Given the availability of a myriad of data modeling software, it’s easy to get overwhelmed. Here is a list of the 10 best data modeling tools to consider:

Astera

Astera is an end-to-end data management solution powered by automation and artificial intelligence (AI) features. It offers several modules and features that streamline unstructured data extraction, data integration, data warehousing, and API and EDI management. All its modules are 100% no-code, which means it can be used by technical and business users alike.

Within the Astera Data Stack, Astera Data Warehouse Builder (ADWB) is a powerful data modeling tool that enables users to:

• Build platform agnostic data models from scratch by reverse engineering existing databases, files, or other sources using a visual data modeler.
• Leverage Dimensional Modeling, Data Vault 2.0, and 3NF schema.
• Automate the Data Vault modeling process and create Hubs, Links, and Satellites for every underlying entity.
• Automate complicated data model operations such as syncing, merging, applying to databases, unifying models, importing data, and copying databases.
• Assign an entity type to each general entity in a data model to turn it into a dimensional model.
• Assign dimension roles to each field, including surrogate keys, business keys, and slowly changing dimensions.
• Verify the data model’s structure as well as data in case of physical models via built-in verification.
• Consolidate data models from different sources into a unified model.
• Deploy data models on Astera and take advantage of built in data governance capabilities.
• Apply changes to databases in one click or generate diff and apply changes.
• Migrate from one database to another in just a few clicks.
• Take advantage of indexes and constraints management.
• Use built in profiler to detect primary and foreign keys.

And much more—all without writing a single line of code.

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Idera ER/Studio

ER/Studio is a comprehensive data modeling tool that enables organizations to manage enterprise metadata. Specifically, it allows data modelers to create and manage data models. ER/Studio offers features such as metadata management, data lineage, and data governance in addition to data modeling.

Pros

• The ability to reduce physical data modeling time using the naming conventions template.
• Allows users to compare their data models to databases for deployments as well as other data models.
• Offers support for creating custom macros.

Cons

• Users frequently report facing difficulties in eliminating orphaned relationships.
• Merging keys introduces multiple bugs.
• Lack of support for conceptual data modeling

Best use case scenario: creating and managing database designs.

DbSchema Pro

DbSchema is another database design and management tool that offers a visual UI to create and manage database structures. Users can use the UI to design tables, define relationships, and specify data types and constraints.

Pros

• The ability to visualize data.
• Offers smart query builder.
• Supports a wide range of databases.

Cons

• The tool is not well-known in the market, raising questions about customer support.
• The documentation is often outdated.

Best use case scenario: For database administrators well-versed with complex scripts.

Toad Data Modeler

Toad Data Modeler is a database modeling and design tool aimed at database administrators and data architects. Like other tools, Toad Data Modeler offers the ability to maintain and document new and existing databases. It also allows users to deploy changes made to the data structures across various platforms.

Pros

• Ability to add newly created objects to workspaces via Auto Complete.
• Visual UI keeps data modeling simple.
• Ability to extract only the required data from large databases.

Cons

• Toad Data Modeler is not easy to set up.
• The tool lags behind when it comes to debugging features.
• Not the best solution for small businesses.

Best use case scenario: Aimed at developers to create and maintain data models.

IBM InfoSphere Data Architect

InfoSphere Data Architect is IBM’s data modeling and design solution for enterprises. Users can expect to create models for different database systems, including SQL Server, Oracle, Db2, MySQL, Informix, and Teradata. InfoSphere Data Architect takes a collaborative approach to data modeling and design as it enables users to share models easily.

Pros

• Ability to integrate with the InfoSphere platform for metadata management.
• Users can tailor data models to their specific needs, for example creating custom attributes to track industry-specific data.
• Leverages parallel processing to handle large volumes of data.

Cons

• The extensive features and functionality require a steeper learning curve.
• Performance can be slower for large volumes of unstructured data compared to specialized tools.
• Some users report limitations in customizing functionalities to specific needs.

Best use case scenario: Enterprise-grade data modeling and design for organizations heavily invested in IBM ecosystem.

Erwin Data Modeler

erwin Data Modeler, now owned by Quest Software, is an enterprise data modeling tool used for various tasks related to data design, discovery, documentation, standardization, and deployment. Besides enabling users to build logical and physical data models, it also facilitates collaborative efforts across teams.

Pros

• erwin’s UI is Intuitive for developers at the beginner level.
• Variety of different ways to view data models visually.
• Assists users in generating deployment scripts without having to worry about syntax.

Cons

• Easily one of the more expensive data modeling tools.
• The fact that users still need to generate deployment scripts means that it’s not a 100% no-code data modeling tool.
• Not the best UI to manage large diagrams.
• Limited variety of export options.

Best use case scenario: Data modeling with collaboration across teams.

MySQL Workbench

MySQL Workbench is an open-source data modeling solution that combines SQL development and database modeling, design, and maintenance. Like other MySQL data modeling tools, it is aimed at database architects, developers, and DBAs to work with the MySQL relational database system. The platform enables users to create visual representations of their database schema using entities, relationships, and attributes.

Pros

• Organizations invested in the MySQL ecosystem can quickly transition logical and physical data models into MySQL databases.
• The community edition is free to use and is supported by the community.
• Offers a relatively easy-to-learn UI compared to some of the more complex data modeling tools.

Cons

• Primarily focused on MySQL, lacking native support for other popular databases like PostgreSQL or Oracle.
• While comprehensive, certain features like data governance might require further tools for complex scenarios.
• Offers limited customization for functionalities compared to other data modeling software, especially open-source tools.
• While the UI is easy-to-learn, it’s still not 100% no-code.

Best use case scenario: Designing and developing MySQL databases for various applications.

Oracle SQL Developer Data Modeler

Oracle SQL Developer Data Modeler is a free graphical tool used for data modeling, design, and reverse engineering within the Oracle ecosystem. It allows users to create, visualize, and document data models of various types, including logical, relational, physical, multidimensional, and data type.

Pros

• Offers tight integration with Oracle ecosystem.
• Simplifies workflows by translating between models and databases.

Cons

• Primarily focused on Oracle, lacks direct support for other popular databases.
• Lacks advanced functionalities for complex data modeling projects.
• Users report performance issues when handling very large and complex data models.

Best use case scenario: Data modeling within Oracle’s ecosystem.

SAP PowerDesigner

Previously owned by Sybase, SAP PowerDesigner is a comprehensive data modeling tool catering to various data management needs, including data modeling, design, documentation, and transformation. SAP PowerDesigner offers a visual data modeling UI that allows users to create logical and physical data models.

Pros

• Ability to integrate with other SAP solutions like BusinessObjects and Data Services.
• Supports high-level business views and technical database-specific models.
• Offers built-in components for common scenarios.

Cons

• The UI is less intuitive compared to newer tools.
• The documentation is not comprehensive.
• The initial set up is difficult and requires technical support, which the users report is not the best.

Best use case scenario: Enterprise-level data modeling for organizations with the SAP ecosystem.

SQL Database Modeler

SQL Database Modeler is one of many visual data modeling tools for database architects, developers, and analysts. Users can expect to create, manage, and document the structure of relational databases for various platforms, such as MySQL, SQL Server, PostgreSQL, etc.

Pros

• Support for multiple relational databases.
• Users use drag-and-drop functionality to create models.

Cons

• Given the focus on relational databases, SQL Database Modeler may not be suitable in scenarios that extensively use NoSQL databases.
• While the basic UI is generally easy, the overall learning curve is steep.
• While it offers a free version, it’s largely limited in functionality.

Best use case scenario: Designing, documenting, and managing relational databases across various platforms.

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How To Select The Right Data Modeling Tool?

So, what are the factors that make investing in data modeling software worthwhile? Something to keep in mind: it’s not a one-size-fits-all situation—a perfect tool doesn’t exist. And the same applies to data modeling tools.

In addition to considering the list of data modeling tools provided above, it’s important to consider additional factors, such as:

Project needs

Where is the data coming from? Whether it’s databases, APIs, flat files, or any other data source, businesses must carefully assess and plan for the origin of their data as the nature of these sources impacts the features and functionality required from a data modeling tool.

Different tools for data modeling offer different range of options. Some tools specialize in one aspect, while others cover the entire spectrum. Therefore, it’s equally important to clearly define whether the data teams need tools for conceptual, logical, or physical data modeling.

Ease of use

Depending on the make-up of their data teams, businesses might need a data model solution with an intuitive and user-friendly interface as a steep learning curve hinders adoption and productivity. Accordingly, it’s important to assess the availability of training resources, documentation, and support from the tool’s vendor and community, especially when considering open-source data modeling solutions.

Integration capabilities

The modern data stack comprises of multiple components, including data modeling tools, which means that compatibility and data integration take the center stage. Evaluate how well the data modeling tool integrates with the existing BI tools, data warehouses, and ETL processes. Modern data modeling tools offer built-in capabilities to connect to a variety of data sources and destinations via native connectors.

At the same time, ensure that the tool supports the databases used by the organization’s data teams. This also includes checking for compatibility with both SQL and NoSQL databases.

Feature-set

Another question to ask is: Does the data modeling software offer all the needed features, like the ability to build entity-relationship diagrams (ERDs) and support for built-in data transformations? A comprehensive data modeling tool should also be equipped with an intuitive user interface. It must provide functionalities that simplify the data modeling process. Look for features such as support for various database management systems, version control for tracking changes in data models, and collaborative features for team-based development.

A comprehensive solution

In SaaS, many tools and software provide several additional features and capabilities that appear to be nice-to-haves initially. However, as the users get comfortable, these capabilities soon become must-haves. For example, a data warehouse tool that enables businesses to create data models in addition to building a data warehouse from scratch.

The Next Step

So, what do you plan to do with every spec of organizational data your company has accumulated over the years? Do you aim to migrate to a new system, or maybe upgrade an existing one, or, perhaps create an insight-yielding data warehouse? Either way, you will need to create data models to structure your data, and using data modeling software is easily the fastest and easiest way to do it.

Data modeling tools have become an integral part of the modern data stack. These tools simplify and accelerate the database design process via their intuitive UI and advanced features. However, given the availability of a wide variety of data modeling software, the key is to select the solution that best aligns with the business needs.

Are you looking for a powerful data modeling tool with the ability to build a fully functional data warehouse for your organization? Try Astera today.

Build Reliable Data Models with Astera—100% No-Code

Whether you're into Dimensional Modeling for intuitive analytics or Data Vault Modeling for agile scalability, we have you covered. Get the best of both worlds with Astera Data Warehouse Builder. Start your 14-day free trial today.

Download Trial