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Hex vs Sagemaker

By Deepnote Team

Updated on Invalid Date

Hex vs. SageMaker: A Comprehensive Comparison of Data Science Platforms

Data science platforms play a pivotal role in facilitating seamless data analysis, model building, and collaboration. Two prominent platforms, Hex and SageMaker, offer robust features tailored to the needs of data scientists. Let's delve into a comparative analysis across different categories to understand their strengths and capabilities.

Jupyter Compatibility


  • Support: Hex provides excellent support for Jupyter notebooks, allowing users to seamlessly create and run Jupyter notebooks within its environment.
  • Interoperability: Integrates well with JupyterLab, offering a familiar interface for Jupyter users.


  • Jupyter Integration: SageMaker extends support for Jupyter notebooks, enabling users to access SageMaker's functionalities directly within a Jupyter environment.
  • Custom Kernel Support: Allows users to create custom kernels for Jupyter notebooks, enhancing flexibility.

Verdict: Both Hex and SageMaker prioritize Jupyter compatibility, offering conducive environments for Jupyter notebook usage.

Connecting to Your Data


  • Data Source Connectivity: Hex offers multiple connectors, allowing seamless integration with various data sources including databases, cloud storages, and APIs.
  • Data Import: Users can import data from different sources directly into their Hex workspace for analysis.


  • Data Integration: SageMaker provides connectors to AWS data sources like S3, Redshift, and Athena, streamlining access to AWS-based data.
  • Data Ingestion: Allows ingestion from various AWS services, enabling efficient data handling within SageMaker.

Verdict: Hex and SageMaker excel in connecting to data sources, with SageMaker focusing more on integration with AWS-based sources.

Interacting and Visualizing Your Data


  • Visualization Tools: Offers interactive visualization libraries like Plotly, Matplotlib, and Seaborn for comprehensive data visualization.
  • Interactivity: Provides interactive widgets and tools for data exploration within its environment.


  • Visualization Support: SageMaker facilitates data visualization through Matplotlib and other Python-based visualization libraries.
  • Customization: Allows users to deploy custom visualization solutions for tailored data representation.

Verdict: Hex and SageMaker provide solid support for data interaction and visualization, with Hex offering more interactive tools within its environment.

Publishing Your Data


  • Publishing Options: Provides options to publish analyses and visualizations as interactive reports or dashboards, facilitating easy sharing.
  • Embedding Capabilities: Allows embedding visualizations into web applications or documents.


  • Model Deployment: Focuses on deploying machine learning models trained within SageMaker to production environments.
  • API Endpoints: Offers the capability to create endpoints for deployed models, enabling real-time predictions.

Verdict: Hex emphasizes publishing analyses and visualizations, while SageMaker primarily concentrates on deploying machine learning models.



  • Collaboration Features: Offers collaborative functionalities allowing multiple users to work on projects simultaneously.
  • Commenting and Sharing: Allows commenting on specific sections of code or analyses, enhancing collaborative workflows.


  • Collaborative Environments: Provides collaboration features through AWS cloud-based access control and shared resources.
  • Version Control: Integrates with Git for version control and collaboration among team members.

Verdict: Both platforms provide collaborative features, with Hex focusing more on in-platform collaboration, while SageMaker leverages AWS services for collaboration and version control.


Hex and SageMaker stand as robust data science platforms, each excelling in different aspects. Hex emphasizes Jupyter compatibility, seamless data connectivity, interactive data exploration, and easy publishing, ideal for analysts and data scientists. SageMaker, deeply integrated with AWS services, shines in model deployment, AWS data integration, and collaborative functionalities, making it suitable for scalable machine learning workflows within an AWS environment.

Ultimately, the choice between Hex and SageMaker depends on specific project needs, preferences in terms of data sources, analysis requirements, collaboration styles, and integration with existing infrastructure.


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