Best Data Notebooks for Product Teams in 2026
Product analytics tools answer the questions they were set up to answer. The moment a PM asks something slightly different, such as how retention differs for accounts that connected an integration in week one, the answer usually lives in the warehouse and requires SQL. Data notebooks give PMs and analysts a place to query, analyze, visualize, and share that work.
We evaluated notebooks on SQL and Python support, warehouse connectivity, AI assistance for less technical users, collaboration and sharing, and how easily an analysis becomes a reusable report or app. Several tools have added strong AI features that let PMs write queries in plain English, which changes who can self-serve.
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Hex
Collaborative SQL and Python notebooks with AI
Hex combines SQL, Python, and no-code cells in a collaborative notebook connected to your warehouse. Its AI features can write queries and explain results, and any notebook can be published as an interactive app or dashboard. It has become a common choice for analytics teams that want PMs to self-serve.
Pros
- SQL, Python, and no-code cells in one notebook
- AI assistance for writing and explaining queries
- Publish analyses as interactive apps
- Strong warehouse integrations
Cons
- Costs rise with editor seats
- Power features require some technical skill
- Governance depends on how the data team sets it up
Deepnote
Collaborative notebooks with an AI data agent
Deepnote is a Jupyter-compatible collaborative notebook with SQL and Python, real-time collaboration, scheduling, and AI features that can write and run code. Notebooks can be published as apps. It has an open-source local version alongside the cloud product.
Pros
- Jupyter-compatible
- Real-time collaboration
- AI agent for writing and running analysis
- Scheduling and app publishing
Cons
- Smaller ecosystem than Jupyter itself
- Less polished BI features than dedicated tools
- Compute costs on heavier workloads
Mode
SQL-first analysis and reporting
Mode, now part of ThoughtSpot, combines a SQL editor, Python and R notebooks, and report building. It is popular with analytics teams that write SQL first and share reports with stakeholders, and it connects to all major warehouses.
Pros
- Strong SQL editor and reporting
- Python and R notebooks for deeper analysis
- Good for sharing reports with stakeholders
- Mature warehouse connectivity
Cons
- Less focus on non-technical self-serve
- Product direction tied to ThoughtSpot
- Pricing geared toward teams
Jupyter
The open-source standard for notebooks
Jupyter Notebook and JupyterLab are the open-source foundation of the notebook world, supporting Python, R, Julia, and many other languages. They run locally or on hosted services, and nearly every data tool can export to or import from the Jupyter format.
Pros
- Free and open source
- Huge ecosystem of libraries and extensions
- Runs anywhere
- Universal file format
Cons
- Collaboration requires extra tooling
- No built-in warehouse governance
- Steeper for non-technical users
Count
Canvas-based analysis for collaborative data work
Count swaps the linear notebook with a canvas where SQL and visual cells can be laid out side by side, connected, and discussed. It is designed for collaborative problem solving, such as investigating a metric drop with a PM and analyst working on the same canvas.
Pros
- Canvas layout suits exploratory investigations
- Strong for live collaboration
- SQL and visual cells together
- Good for explaining analysis to stakeholders
Cons
- Different mental model from notebooks
- Smaller community
- Less suited to heavy Python work
Google Colab
Free hosted notebooks with Gemini built in
Google Colab provides hosted Jupyter notebooks with free compute and Gemini AI assistance, and connects easily to BigQuery and Google Drive. It is the fastest way to start analyzing data with Python without installing anything.
Pros
- Free with no setup
- Gemini assistance for code
- Easy BigQuery and Drive access
- Shareable like a Google Doc
Cons
- Sessions time out
- Limited team governance
- Less suited to production reporting