Best Data Modeling Tools for Google Cloud Platform

Find and compare the best Data Modeling tools for Google Cloud Platform in 2024

Use the comparison tool below to compare the top Data Modeling tools for Google Cloud Platform on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    Looker Reviews
    Top Pick
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    Looker reinvents the way business intelligence (BI) works by delivering an entirely new kind of data discovery solution that modernizes BI in three important ways. A simplified web-based stack leverages our 100% in-database architecture, so customers can operate on big data and find the last mile of value in the new era of fast analytic databases. An agile development environment enables today’s data rockstars to model the data and create end-user experiences that make sense for each specific business, transforming data on the way out, rather than on the way in. At the same time, a self-service data-discovery experience works the way the web works, empowering business users to drill into and explore very large datasets without ever leaving the browser. As a result, Looker customers enjoy the power of traditional BI at the speed of the web.
  • 2
    Xplenty Reviews

    Xplenty

    Xplenty Data Integration

    Xplenty is a scalable data delivery and integration software that allows large businesses and SMBs to prepare and transfer data to the cloud for analytics. Xplenty features include data transformations and drag-and-drop interface. It also integrates with over 100 data storages and SaaS apps. Developers can easily add Xplenty to their data solution stack. Xplenty allows users to schedule jobs, track job progress, and keep track of job status.
  • 3
    Navicat Data Modeler Reviews

    Navicat Data Modeler

    Navicat

    $22.99 per month
    Navicat Data Modeller is a powerful, cost-effective database design tool that helps you create high-quality conceptual and logical data models. It can be used to create database structures, reverse/forward engineer processes, import models directly from ODBC data sources, create complex SQL/DDL, and print models to files. It makes it easy to create complex entity relationship models. You can also generate script SQL in just one click. Navicat Data Modeler supports many database systems including MySQL, MariaDB and Oracle, SQL Servers, PostgreSQL and SQLite. Professional object designers are available for Tables or Views. They can help you create, modify, and design models. You'll always know what you are doing, even if you don't need to write complex SQL. Navicat Data Modeler also supports three standard notations: Crow's Foot (IDEF1x), UML (Unified Modeling Language) and UML.
  • 4
    Databricks Data Intelligence Platform Reviews
    The Databricks Data Intelligence Platform enables your entire organization to utilize data and AI. It is built on a lakehouse that provides an open, unified platform for all data and governance. It's powered by a Data Intelligence Engine, which understands the uniqueness in your data. Data and AI companies will win in every industry. Databricks can help you achieve your data and AI goals faster and easier. Databricks combines the benefits of a lakehouse with generative AI to power a Data Intelligence Engine which understands the unique semantics in your data. The Databricks Platform can then optimize performance and manage infrastructure according to the unique needs of your business. The Data Intelligence Engine speaks your organization's native language, making it easy to search for and discover new data. It is just like asking a colleague a question.
  • 5
    ZinkML Reviews

    ZinkML

    ZinkML Technologies

    ZinkML is an open-source data science platform that does not require any coding. It was designed to help organizations leverage data more effectively. Its visual and intuitive interface eliminates the need for extensive programming expertise, making data sciences accessible to a wider range of users. ZinkML streamlines data science from data ingestion, model building, deployment and monitoring. Users can drag and drop components to create complex pipelines, explore the data visually, or build predictive models, all without writing a line of code. The platform offers automated model selection, feature engineering and hyperparameter optimization, which accelerates the model development process. ZinkML also offers robust collaboration features that allow teams to work seamlessly together on data science projects. By democratizing the data science, we empower businesses to get maximum value out of their data and make better decisions.
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