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Notebook Alternatives

4 posts · Jul 2022 – Dec 2025

What this trail covers

Hex has evolved its data analysis capabilities by providing a comprehensive guide to Exploratory Data Analysis (EDA). This includes detailing EDA techniques such as data profiling, statistical summaries, and visualization, and emphasizing their importance in understanding data before building models or dashboards. The post also highlights how collaborative tools and AI assistants are transforming EDA workflows, enabling faster iteration and insight generation. Best practices for conducting effective EDA are covered, positioning Hex as a modern alternative to traditional notebook environments by addressing collaboration, reproducibility, and publishing challenges inherent in tools like Jupyter.

All Hex trails
  1. Best Jupyter alternatives comparedThis post compares Hex to other notebook alternatives (Google Colab, marimo, Databricks) by highlighting how Hex addresses the limitations of traditional Jupyter notebooks, specifically around collaboration, execution state, and sharing. It details Hex's real-time multiplayer editing, AI-assisted development, unified SQL/Python environment, semantic layer integration, and one-click publishing to interactive apps as key differentiators. The post frames Hex as a solution for data teams needing a consolidated platform for exploration to stakeholder delivery, and for SQL-fluent analysts.
  2. Beyond linear notebooksThis post introduces the concept of reactive notebooks and their advantages over traditional imperative notebooks, specifically addressing issues with shared mutable state, interpretability, reproducibility, and performance. It details how Hex implements a DAG-based approach by parsing code to generate a semantic representation and build a DAG of cell dependencies, enabling automatic re-execution and state synchronization. Usability improvements like hiding import edges and merging duplicate edges in the graph UI are also mentioned.
  3. Notebooks weren't built for the modern data stackThis post critiques traditional computational notebooks, highlighting their limitations in handling the scale of modern cloud data warehouses, their broken state/execution model leading to interpretability and reproducibility issues, and their inadequacy for team collaboration and sharing with stakeholders. It argues that notebooks, while having potential, are not built for the modern data stack and sets the stage for Hex's approach to address these shortcomings.