Best Data Modeling Tools for Google Cloud Platform
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Compare the Top Data Modeling Tools that integrate with Google Cloud Platform as of May 2026
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This a list of Data Modeling tools that integrate with Google Cloud Platform. Use the filters on the left to add additional filters for products that have integrations with Google Cloud Platform. View the products that work with Google Cloud Platform in the table below.
What are Data Modeling Tools for Google Cloud Platform?
Data modeling tools are software tools that help organizations design, visualize, and manage data structures, relationships, and flows within databases and data systems. These tools enable data architects and engineers to create conceptual, logical, and physical data models that ensure data is organized in a way that is efficient, scalable, and aligned with business needs. Data modeling tools also provide features for defining data attributes, establishing relationships between entities, and ensuring data integrity through constraints. By automating aspects of the design and validation process, these tools help prevent errors and inconsistencies in database structures. They are essential for businesses that need to manage complex datasets and maintain data consistency across multiple platforms. Compare and read user reviews of the best Data Modeling tools for Google Cloud Platform currently available using the table below. This list is updated regularly.
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1
Looker
Google
Looker, Google Cloud’s business intelligence platform, enables you to chat with your data. Organizations turn to Looker for self-service and governed BI, to build custom applications with trusted metrics, or to bring Looker modeling to their existing environment. The result is improved data engineering efficiency and true business transformation. Looker is reinventing business intelligence for the modern company. Looker works the way the web does: browser-based, its unique modeling language lets any employee leverage the work of your best data analysts. Operating 100% in-database, Looker capitalizes on the newest, fastest analytic databases—to get real results, in real time. -
2
Navicat Data Modeler
Navicat
Navicat Data Modeler is a powerful and cost-effective database design tool which helps you build high-quality conceptual, logical and physical data models. It allows you to visually design database structures, perform reverse/forward engineering processes, import models from ODBC data sources, generate complex SQL/DDL, print models to files, and much more. Simplify the task of creating complex entity relationship models and generate the script SQL with a simple click. Navicat Data Modeler supports various database systems, including MySQL, MariaDB, Oracle, SQL Server, PostgreSQL, and SQLite. Create, modify, and design your models using professional object designers, available for Tables and Views. Without the need to write complex SQL to create and edit objects, you’ll know exactly what you are working on. Also, Navicat Data Modeler supports three standard notations, Crow’s Foot, IDEF1x and UML.Starting Price: $22.99 per month -
3
Timbr.ai
Timbr.ai
Timbr is the ontology-based semantic layer used by leading enterprises to make faster, better decisions with ontologies that transform structured data into AI-ready knowledge. By unifying enterprise data into a SQL-queryable knowledge graph, Timbr makes relationships, metrics, and context explicit, enabling both humans and AI to reason over data with accuracy and speed. Its open, modular architecture connects directly to existing data sources, virtualizing and governing them without replication. The result is a dynamic, easily accessible model that powers analytics, automation, and LLMs through SQL, APIs, SDKs, and natural language. Timbr lets organizations operationalize AI on their data - securely, transparently, and without dependence on proprietary stacks - maximizing data ROI and enabling teams to focus on solving problems instead of managing complexity.Starting Price: $599/month -
4
Xplenty
Xplenty Data Integration
Xplenty, a scalable data integration and delivery software, allows SMBs and large enterprises to prepare and transfer data for analytics to the cloud. Xplenty features include data transformations, drag-and-drop interface, and integration with over 100 data stores and SaaS applications. Xplenty can be added by developers to their data solution stack with ease. Xplenty also allows users to schedule jobs and monitor job progress and status. -
5
Databricks
Databricks
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. The winners in every industry will be data and AI companies. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals. Databricks combines generative AI with the unification benefits of a lakehouse to power a Data Intelligence Engine that understands the unique semantics of your data. This allows the Databricks Platform to automatically optimize performance and manage infrastructure in ways unique to your business. The Data Intelligence Engine understands your organization’s language, so search and discovery of new data is as easy as asking a question like you would to a coworker. -
6
ZinkML
ZinkML Technologies
ZinkML is a zero-code data science platform designed to address the challenges faced by organizations in leveraging data effectively. By providing a visual and intuitive interface, it eliminates the need for extensive coding expertise, making data science accessible to a broader range of users. ZinkML streamlines the entire data science lifecycle, from data ingestion and preparation to model building, deployment, and monitoring. Users can drag-and-drop components to create complex data pipelines, explore data visually, and build predictive models without writing a single line of code. The platform also offers automated feature engineering, model selection, and hyperparameter tuning, accelerating the model development process. Moreover, ZinkML provides robust collaboration features, enabling teams to work together seamlessly on data science projects. By democratizing data science, we empower companies to extract maximum value from their data and drive better decision-making.
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