What is Mode Analytics?
Mode Analytics is a business-intelligence and reporting tool built around a SQL editor, embedded Python and R notebooks, and shareable interactive reports and dashboards, aimed at data analysts rather than pure drag-and-drop BI users. Mode describes itself as the intelligence layer for your data stack. ThoughtSpot acquired Mode for $200 million in a deal announced in June 2023 and completed in July 2023, and Mode continues to operate as a distinct product within ThoughtSpot.
Analysts write SQL, load the results straight into connected R and Python notebooks for advanced analytics, and explore data visually in the browser. Governed datasets are built by writing SQL and can be cached in Mode's in-memory data engine so drag-and-drop explorations do not hit the warehouse. Mode connects to most major data warehouses and can use metrics defined in the dbt Semantic Layer. By design it does not build connectors to SaaS tools, which are expected to be loaded into the warehouse first.
Mode is a hosted service. The plan comparison lists Studio for individual analysts with SQL, Python and R, private database connections and up to 3 users, Pro for collaborating teams with schedules, permissioning, Email and Slack sharing and API access, and Enterprise with advanced SSO and identity management. White-label embeds are offered as an add-on. The site says a team can be up and running in 30 minutes or less.
Key capabilities
- SQL editor Query directly against connected warehouses.
- Notebooks Python and R analysis alongside SQL.
- Interactive reports Drag-and-drop charts and shareable dashboards built on query results.
Where Mode Analytics fits
- SQL editor Analysts dive into data with SQL and iterate on queries quickly to deliver results.
- Python and R notebooks SQL results load directly into the connected R and Python notebook to start advanced analytics.
- Visual exploration Data teams and business users explore 100K+ datapoints visually in the browser, then share the explorations.
- Writeups A report can combine explorable charts, output cells and a narrative that recommends what comes next.
- Datasets Verified, canonical datasets are defined with SQL, so one definition feeds multiple reports and reduces database load.
- Embedded analytics Branded analytics can be embedded in your application, with charts styled through CSS.
- dbt metrics Metrics defined in the dbt Semantic Layer can be used for more scalable reporting.
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