Mode
Mode is a SQL notebook tool with BI dashboards on top. Analysts write SQL queries, visualize the results, and share reports with stakeholders. It also has Python notebooks for deeper analysis. The trade-off: it stays SQL-first, so business users cannot explore data without writing queries. That makes it popular with technical teams but limits self-service adoption.
Official site ↗Why it ranks here
- SQL-first workflow is faster for technical users than drag-and-drop BI tools.
- Notebooks let you document analysis alongside queries and charts, which is good for reproducibility.
- Shared query library means teams can reuse SQL snippets for common metrics like ARR or pipeline.
Where it sits
- More technical than Tableau, Power BI, or Sigma; less accessible for business users.
- Similar to Hex in notebook UX; Mode is SQL-focused, Hex adds Python and interactive apps.
- Weaker semantic layer than Looker or Omni; metrics live in individual queries, not a governed model.
How GTM Operations teams use it
- RevOps analysts prototype pipeline dashboards in SQL notebooks, then share static reports with sales leadership.
- Marketing data analysts join campaign data with opportunity data to measure ROI, documenting assumptions in the notebook.
- Weekly pipeline reviews using Mode reports with SQL queries that count opportunities by stage and close date.
- Ad-hoc deep dives into data quality issues, with Python notebooks for fuzzy matching and anomaly detection.
In-depth notes
- Pricing is per-editor. Expect mid-tier per-editor monthly pricing; viewers can access reports for free or at lower cost depending on the plan.
- Python notebooks are a differentiator, but the integration is basic: you query with SQL, then pass results to Python. It is not a full data science platform.
- Mode does not have a semantic layer. If you want to ensure every team uses the same ARR definition, you need to enforce it through shared queries or dbt models.
- Query performance depends on your warehouse. Mode does not optimize or cache queries beyond basic result caching.
- Good fit for technical RevOps or data teams that prefer SQL over drag-and-drop BI. If your stakeholders want self-service, Mode will not scale.
Best for
SQL-literate RevOps teams that want fast analysis and reproducible reports, or data teams that need Python notebooks.
Avoid if
You want self-service BI for business users (use Tableau, Sigma, or Omni), or you need a governed semantic layer (use Looker).
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. #7 
ThoughtSpot Strong Search-driven analytics with natural language queries; AI-powered insights for business users who do not want to build dashboards. #8 
Hex Situational Notebooks for Python and SQL hybrid analysis; build interactive apps from notebooks with code and no-code cells. #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 Mode to your warehouse, write a SQL query in a notebook, and visualize the results with charts. Share the report with stakeholders or schedule it to run on a cadence.
First thing to build
Write a SQL query to count opportunities by stage and close date, then add a bar chart and line chart to the notebook. Document assumptions in markdown cells and share as a weekly pipeline report.
What actually matters
- Build a shared query library for common metrics like ARR and pipeline
- Use SQL parameters to let stakeholders adjust filters without editing queries
- Schedule reports to run and email results to stakeholders
- Add Python cells for custom analysis like fuzzy matching or forecasting
Watch out
Metrics drift across notebooks if definitions are not centralized, so enforce consistency via dbt models or shared queries.