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How to create a Recipe in CRM Analytics

Create a recipe in Data Manager to combine and clean source data into a dataset that dashboards can query. You build the pipeline on a visual canvas, preview as you go, then save and run.

By Dipojjal Chakrabarti · Founder & Editor, Salesforce DictionaryLast updated Jun 16, 2026

Create a recipe in Data Manager to combine and clean source data into a dataset that dashboards can query. You build the pipeline on a visual canvas, preview as you go, then save and run.

  1. Open Data Prep

    In Analytics Studio, go to Data Manager and choose Recipes, then click Create Recipe. The visual canvas opens with a prompt to add your first input.

  2. Add input data

    Add one or more input nodes. Select a dataset, pull directly from a Salesforce object, or read from a configured external connection to bring source rows onto the canvas.

  3. Join, filter, and transform

    Click the plus button to add nodes. Use a join to add related columns, a filter to drop unwanted rows, and a transform node to build calculated columns, convert types, or bucket values.

  4. Preview the result

    Select a node to preview sample rows at that point in the pipeline. Confirm joins matched and formulas computed correctly before processing the full data volume.

  5. Add an output node

    End the recipe with an output node that writes results to a dataset, a CSV file, Data Cloud, or an external location like Amazon S3. A recipe can have more than one output.

  6. Save, run, and schedule

    Save the recipe, then run it on demand. Set a schedule so it refreshes on a cadence, and watch run status in the monitor area of Data Manager.

Input noderequired

At least one source of data: an existing dataset, a Salesforce object, or an external connection that has been set up.

Output noderequired

A target for the prepared data, such as a dataset name, so the recipe has somewhere to write its result.

Recipe namerequired

A clear, descriptive label that tells teammates what the recipe prepares and which dataset it feeds.

Gotchas
  • A recipe cannot save or run without an output node, so the pipeline must end somewhere.
  • Joins can multiply rows when keys are not unique on the lookup side, which inflates measures downstream; check row counts in preview.
  • Convert text that holds dates or numbers to real date and measure types, or charts and math will not work as expected.
  • A dashboard reads the dataset, not the recipe, so the dataset only changes after the recipe runs again.

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