Hex
Hex is a notebook platform that combines SQL, Python, and visual charts in one interface. Analysts write code to pull and transform data, then turn notebooks into interactive apps for stakeholders with filters and parameters. It is developer-friendly and good for custom analysis, but it requires Python skills and does not replace a traditional BI tool for self-service dashboards.
Official site ↗Why it ranks here
- Notebooks are better than SQL-only tools (Mode) for custom analysis that needs Python for fuzzy matching, forecasting, or complex transformations.
- Interactive apps let you share analysis with stakeholders who can adjust parameters without touching code.
- Code and no-code cells in the same notebook make it accessible to semi-technical users.
Where it sits
- More flexible than Mode for custom analysis; less accessible for business users than Tableau or Sigma.
- Similar to Jupyter notebooks but with better collaboration and sharing features.
- Not a replacement for BI tools; Hex is for analysis and prototyping, not production dashboards.
How GTM Operations teams use it
- RevOps analysts build churn prediction models in Python, then share interactive apps with CS leadership to explore cohorts.
- Marketing data scientists join campaign data with opportunity data, run attribution models in Python, and publish results as Hex apps.
- Ad-hoc deep dives into data quality issues, using Python libraries like pandas and fuzzywuzzy for matching and cleaning.
- Prototyping dashboards before committing to a BI tool: build the logic in Hex, validate with stakeholders, then rebuild in Tableau or Looker.
In-depth notes
- Pricing is per-editor. Expect mid-tier per-editor monthly pricing; viewers can access published apps at lower cost.
- Python dependency management is built-in: specify packages in the notebook, and Hex installs them in a managed environment.
- SQL support is strong: query your warehouse directly from a cell, then pass results to Python for analysis.
- Not a replacement for dbt: Hex is for analysis, not production data pipelines. Use dbt for transformation, then query the results in Hex.
- Good fit for RevOps teams with Python skills that need custom analysis beyond what BI tools offer.
Best for
Technical RevOps or data science teams that need Python for custom analysis, or orgs that want to share interactive apps without building BI dashboards.
Avoid if
You need self-service BI for non-technical users (use Tableau or Sigma), or your team does not know Python (use Mode for SQL-only).
The rest of BI & analytics
Tableau Leader Visual analytics platform with drag-and-drop interface; Salesforce-owned market leader for interactive dashboards. #2 
Looker Leader Semantic layer (LookML) on live warehouse data; Google Cloud-owned platform for governed analytics with code-based modeling. #3 
Power BI Leader Microsoft's BI platform with deep Office and Teams integration; cost-effective for Microsoft shops with enterprise agreements. #4 
Omni Strong Modern warehouse-native BI with spreadsheet, SQL, and semantic model workflows; live queries with no extracts or cubes. #5 
Sigma Strong Warehouse-native BI with a spreadsheet interface; live queries on Snowflake, BigQuery, or Databricks with familiar Excel-like UX. #6 
Mode Strong SQL-first analytics platform with notebooks for analysis and reporting; popular with technical RevOps and data teams. #7 
ThoughtSpot Strong Search-driven analytics with natural language queries; AI-powered insights for business users who do not want to build dashboards. #9 
Domo Situational All-in-one platform with ETL, cloud data warehouse, and BI; higher total cost but unified vendor for orgs without a data stack. #10 
Qlik Situational Associative analytics engine for exploring complex data relationships; in-memory architecture with a steeper learning curve. #11 
Metabase Situational Open-source BI with simple setup and no-code query builder; good for small teams that need dashboards without enterprise complexity. #12 
Lightdash Niche dbt-native BI tool; metrics and dimensions defined in dbt YAML, dashboards built on transformed warehouse tables. #13 
Preset Niche Managed Apache Superset; open-source BI with commercial support, hosting, and enterprise features. #14 
Redash Niche Open-source SQL query and visualization tool; simple dashboards for technical teams with minimal setup. Learning guide
Setup
Connect Hex to your warehouse, write SQL to pull data, then add Python cells for analysis using pandas or scikit-learn. Turn the notebook into an app with input widgets and share with stakeholders.
First thing to build
Query opportunities from the warehouse, use Python to calculate churn cohorts by close date, and visualize retention curves with matplotlib. Add input widgets for date range and share as a Hex app.
What actually matters
- Use SQL cells to query the warehouse and pass results to Python for analysis
- Add input widgets like dropdowns and sliders to make apps interactive
- Schedule notebooks to run and email results on a cadence
- Version control notebooks in Git for reproducibility
Watch out
Notebooks become brittle if dependencies or data schemas change, so pin package versions and test regularly.