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Hex

Hex is a collaborative data workspace where teams unite to explore, analyze, and visualize data. It blends SQL, Python, and no-code tools with AI assistance, enabling seamless end-to-end workflows from raw data to interactive dashboards and data applications.

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Introduction

What is Hex?

Hex represents the next generation of data science platforms, creating a unified workspace where data professionals and business users collaborate effectively. This interactive notebook environment brings together SQL and Python capabilities, supercharged by AI features such as Hex Magic that automate query generation, error correction, and visualization design. The platform ensures smooth connectivity with data storage systems and workflow management tools while delivering enterprise-level security and diverse deployment choices. Hex streamlines the entire data journey from initial exploration to production-ready dashboards and applications, driving organizational agility and data-informed decision making.

Key Features:

• Unified SQL and Python Environment: Execute SQL commands and Python scripts interchangeably within an adaptable notebook interface featuring sophisticated editing capabilities and dependency management.

• AI-Enhanced Productivity (Hex Magic): Utilizes advanced language models contextualized to your data landscape to automatically create queries, resolve errors, design visualizations, and streamline processes.

• Team-Oriented Data Platform: Enable simultaneous multi-user editing with change tracking, access controls, and distinct commenting systems for both development code and published content.

• Comprehensive Data Connectivity: Pre-built integrations with leading data platforms and pipeline tools including dbt, Airflow, and Snowpark, complemented by customizable API interfaces.

• Enterprise Security and Compliance: Certified for SOC2 and HIPAA standards with single sign-on, automated user provisioning, and encrypted database connections across multi-tenant, dedicated tenant, and private cloud configurations.

• Dynamic Data Applications: Develop customizable, interactive analytics dashboards using intuitive drag-and-drop components and extensive visualization options.

Use Cases:

• Data Discovery and Investigation: Conduct comprehensive data exploration combining SQL queries, Python analytics, and AI support to rapidly identify patterns and insights.

• Interactive Data Sharing: Create and distribute dynamic reports and dashboards that enable both technical experts and business users to gain actionable intelligence.

• Predictive Analytics and ML Development: Construct and deploy machine learning models using built-in Python capabilities and integrated data workflows within a consolidated environment.

• Streamlined Data Automation: Implement AI-driven automation for query creation, issue resolution, and visualization refreshes to minimize manual tasks and accelerate project timelines.

• Regulatory Compliance and Data Management: Maintain strict data governance and compliance requirements through enterprise security protocols ideal for regulated sectors.