Omni
Omni is a newer BI tool built for cloud warehouses (Snowflake, BigQuery, Databricks). It combines a spreadsheet interface for end users, a SQL editor for analysts, and a semantic model layer (similar to LookML but lighter). Every query runs live on the warehouse; there are no extracts. The trade-off: it is less mature than Tableau or Looker, and the vendor is smaller.
Official site ↗Why it ranks here
- Warehouse-native architecture means no data movement; dashboards query Snowflake or BigQuery directly.
- Spreadsheet interface lowers the bar for business users who do not want to learn drag-and-drop BI tools.
- Semantic model layer lets you define reusable metrics without the full complexity of LookML.
Where it sits
- More modern than Tableau or Power BI for warehouse-native workflows; less mature in features and ecosystem.
- Lighter semantic layer than Looker; easier to adopt, but less governance at scale.
- Similar to Sigma in spreadsheet UX; Omni adds a stronger model layer.
How GTM Operations teams use it
- RevOps builds semantic models for pipeline, bookings, and churn; sales ops then explores data in the spreadsheet interface.
- Marketing attribution dashboards that join Snowflake campaign data with Salesforce opportunities.
- Weekly scorecards with live data from the warehouse, shared in Slack or embedded in Notion.
- Ad-hoc analysis by RevOps analysts using the SQL editor, then turning queries into dashboards.
In-depth notes
- Pricing is per-editor and per-viewer. Expect mid-tier per-editor monthly pricing and lower per-viewer costs; exact numbers are not public.
- Model layer uses a YAML syntax similar to LookML but simpler. You define dimensions, measures, and joins; users then pivot and filter in the UI.
- Performance depends on your warehouse. Omni pushes all computation to the warehouse, so materialized views and clustering matter.
- Smaller vendor risk: Omni is a startup (founded 2020s). If you need enterprise support and a long track record, Tableau or Looker is safer.
- Good fit for data-forward orgs that already invested in Snowflake or BigQuery and want a modern BI layer without extract overhead.
Best for
Warehouse-native teams that want a lighter semantic layer than Looker, or orgs tired of Tableau extract refreshes.
Avoid if
You need enterprise-grade vendor stability (use Tableau, Looker, or Power BI), or your data is not in a cloud warehouse.
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. #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. #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 Omni to Snowflake or BigQuery, define a model with views, dimensions, and measures in YAML. Users then explore data in the spreadsheet interface or build dashboards.
First thing to build
Model opportunities with fields like stage, amount, and close date as a YAML view. Define a pipeline measure as sum of amount where stage is not Closed Lost. Users pivot and filter in the spreadsheet.
What actually matters
- Use materialized views in the warehouse to speed up dashboard queries
- Define row-level security in the model layer using filter templates
- Set dashboard refresh schedules or enable auto-refresh for live data
- Version control model YAML in Git for change tracking
Watch out
Warehouse query costs can spike if users build expensive pivots, so monitor query patterns and add materialized views.