Best Database Design Software for GitHub

Find and compare the best Database Design software for GitHub in 2026

Use the comparison tool below to compare the top Database Design software for GitHub on the market. You can filter results by user reviews, pricing, features, platform, region, support options, integrations, and more.

  • 1
    SmartDraw Reviews
    Top Pick

    SmartDraw

    SmartDraw

    $7.95 per user per month
    533 Ratings
    See Software
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    With SmartDraw, you can effortlessly create a visual diagram of your database by utilizing your data. Just upload a CSV file containing your table definitions from your server, and SmartDraw will automatically produce a relational database diagram—no manual drawing necessary! Once your design is generated, you can easily modify it using user-friendly drag-and-drop features. SmartDraw also allows you to create Entity-Relationship Diagrams (ERDs) from your data, enhance them with additional information, and develop a variety of technical diagrams, including UML, AWS, and Azure designs. Additionally, you can design business diagrams and floor plans with ease. Collaborate with your team in real-time, implement version control as needed, and save your work to platforms like OneDrive, Google Drive, or SharePoint. You can also import files and stencils from Lucidchart and Visio. Enjoy seamless integrations with leading platforms such as Microsoft, Google, and Confluence, among others.
  • 2
    Lucidchart Reviews
    Top Pick

    Lucidchart

    Lucid Software

    $7.95/month/user
    10 Ratings
    Lucidchart is a comprehensive visual collaboration platform that empowers teams to create intelligent, data-driven diagrams for process mapping, team planning, systems architecture, and more. With features like AI-powered diagram generation, real-time collaboration, and data integration, Lucidchart helps users quickly visualize their systems and workflows with ease. Whether you’re building technical diagrams or mapping out complex organizational structures, Lucidchart streamlines the process, saving you time and improving clarity. It integrates with a variety of popular tools such as Jira, Slack, Confluence, and Notion, making it an invaluable tool for enhancing teamwork, driving decisions, and accelerating innovation across your organization.
  • 3
    Hackolade Reviews

    Hackolade

    Hackolade

    €175 per month
    Hackolade Studio is a comprehensive data modeling platform built for today’s complex and hybrid data ecosystems. Originally developed to address the lack of visual design tools for NoSQL databases, Hackolade has evolved into a multi-model solution that supports the broadest range of data technologies in the industry. The platform enables agile, iterative schema design and governance for both structured and semi-structured data, making it ideal for organizations working across traditional RDBMS, modern data warehouses, NoSQL stores, and streaming systems. Hackolade supports technologies such as Oracle, PostgreSQL, BigQuery, Databricks, Redshift, Snowflake, MongoDB, Cassandra, DynamoDB, Neo4j, Kafka (with Confluent Schema Registry), OpenAPI, GraphQL, and more. Beyond databases, Hackolade Studio offers robust capabilities for API modeling, supporting OpenAPI (Swagger) and GraphQL, as well as native modeling for data exchange formats like JSON Schema, Avro, Protobuf, Parquet, and YAML. It also integrates with metadata and data governance platforms like Unity Catalog and Collibra, making it a powerful enabler for organizations focused on data quality, lineage, and compliance. Key features include reverse and forward engineering, schema versioning, data type mapping, and team collaboration tools. Whether you're building data products, managing data contracts, or migrating between systems, Hackolade Studio provides a unified interface for modeling, documenting, and evolving your schemas. Hackolade is trusted by enterprises across finance, retail, healthcare, and telecom to align data architecture with real-world delivery. It’s an essential tool for teams implementing data mesh, data fabric, microservices, or API-first strategies.
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