Rhubarb Knowledge Center
Practical guides for exploring data with AI, finding useful views quickly, and turning the results into fast, custom interactive visualizations.
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22 published resources about getting from a data question to a useful answer and a visualization people can actually use.
If you understand the business question but do not want to spend the next hour building the analysis, upload or connect the data and ask Rhubarb in ordinary language. The assistant can query, slice, compare, and visualize the result; you can keep asking follow-ups until the useful view appears.
Rhubarb helps a small team get useful analysis from the data it already has without first building a large BI operation. Upload a file or connect a source, ask the business question, and turn the useful answers into visualizations the team can keep using and sharing.
Program teams often know the next question they want to ask but have to wait for a reporting cycle or analyst. Rhubarb lets them explore approved data conversationally, see a visual answer quickly, and turn the useful views into a repeatable report.
You do not have to decide on a chart type before exploring the data. Tell Rhubarb what you want to understand, let the assistant test the analysis and visual form, and use the conversation to discover which view is actually useful.
Make the work
From a CSV, database, or survey table to a useful first insight, a custom chart, a map, a Sankey, or a client-ready interactive report.
View all problem guidesStart with the question people need answered, not the chart type. Get the data into a shape you trust, then test a useful first view. Keep only the interactions that make the answer clearer, and check the numbers, mobile layout, and accessibility before you publish.
Treat the CSV as raw material, not a finished dashboard. Upload it, ask the first question in plain English, and let the assistant help profile, reshape, compare, and visualize the data. Keep the definitions honest, then save the useful views as a dashboard or report.
Decide whether people will use a Rhubarb page, an embed, or a visualization delivered inside your own authenticated or paywalled site. Send the browser only the data that view needs, test the real host page on desktop and mobile, and decide how updates and failures will be handled.
Describe what you want to learn and how the visualization should work. Let the assistant prepare the data and build the first custom visual, then inspect the data-source SQL and JavaScript, check the numbers, and edit the code directly when you need more control. You skip the blank-file work without giving up review.
Choose deliberately
Rhubarb is not the best choice for every visualization. These pages explain the boundary clearly.
Flourish is a strong template-led storytelling tool and now has both a plain-language AI Assistant and an LLM Connector. Rhubarb is a stronger fit when you want the AI involved earlier: start with the data question, let it help shape the query and analysis, then build and publish a custom visualization in the same project.
Datawrapper makes it easy to build excellent standard charts, maps, and tables through a focused editor. Rhubarb is a stronger fit when you want to ask the data a question in plain English and have AI help choose fields, write the analysis, and build the useful visualization rather than operating the chart workflow yourself.
Tableau is the stronger fit for broad governed analytics and now has substantial conversational AI through Tableau Agent. Rhubarb is the stronger fit for lean teams that want to move from a plain-English data question to a focused custom visualization quickly, publish only the data that view needs, and keep the data-source SQL and web code visible.
For people who like working with AI
Rhubarb is especially useful for people who already like using AI as a thinking partner. Instead of starting with chart menus, shelves, and field placement, start with the question: “What changed?”, “Which group is different?”, “What should I look at next?” The assistant can do the data work and build a visual answer, while the underlying query and code remain available when you want to inspect them.