Recipe
In CRM Analytics (formerly Tableau CRM, originally Einstein Analytics), a Recipe is a set of data transformation steps that prepares and combines datasets for analysis.
Definition
In CRM Analytics (formerly Tableau CRM, originally Einstein Analytics), a Recipe is a set of data transformation steps that prepares and combines datasets for analysis. Joins, filters, computed columns, and aggregations are defined in a visual editor, each step connected to the next to form a declarative data-prep pipeline - the output is a dataset that dashboards and lenses read from.
In plain English
“A Recipe in CRM Analytics is a visual data pipeline. Drag in your sources, add steps to join, filter, compute, and aggregate, and the output is a clean dataset your dashboards use. Same tool was called Tableau CRM Recipe and Einstein Analytics Recipe in earlier years.”
Worked example
An analytics engineer at Harbor Logistics builds a Recipe for the monthly shipment dashboard: it ingests Shipment records from Salesforce, joins to a Carrier dataset uploaded from a spreadsheet, filters to the trailing 12 months, computes an on-time percentage per lane, aggregates to carrier-and-region level, and outputs a dataset. The recipe runs on a nightly schedule; the monthly dashboard reads the clean dataset instead of re-joining raw data each time someone opens it.
Why Recipe matters
In CRM Analytics (renamed from Tableau CRM in 2022; originally launched as Einstein Analytics), a Recipe is a set of data transformation steps that prepare and combine datasets for analysis. Steps are defined in a visual editor - input, join, filter, transform, aggregate, output - and connected to form a declarative data pipeline. Recipes are how analysts shape raw source data into the form that dashboards and lenses can efficiently query.
Recipes are foundational to CRM Analytics quality because dataset shape determines what analysis is possible. Well-designed recipes produce clean, appropriately denormalized datasets that make dashboards fast and flexible; poorly designed recipes produce data that every downstream dashboard has to work around. Mature analytics teams treat recipe design as first-class data engineering - naming conventions, documentation, and code-review discipline make recipes maintainable as the library grows.
How to create Recipe
Recipes are the data-prep workflows in CRM Analytics — visual transformations that combine datasets, apply filters, compute columns, run aggregations, output a final dataset. Replacement for the older Dataflow tool; modern CRM Analytics development uses Recipes for all data prep. Output is a dataset that lenses / dashboards / Einstein Discovery models read from.
- Open CRM Analytics Studio → Recipes (or App Launcher)
App Launcher → Analytics Studio → Create → Recipe.
- Set Recipe Name and target App
Convention: per-purpose ("Pipeline by Stage," "Customer 360").
- Add input nodes (datasets / Salesforce objects / external sources)
Drag onto canvas. Each input is a starting dataset.
- Add transformation nodes
Join (merge two inputs), Filter (rows), Transform (compute columns), Aggregate (summarize), Append (union). Connect via arrows.
- Configure each node's parameters
Click into a node → set field mappings, filter conditions, formula expressions.
- Add an Output node
Specify the target dataset — what name, which App, which level of permissions.
- Run the Recipe
Click Run to execute the pipeline. First run creates the dataset; subsequent runs refresh it.
- Schedule recurring runs (optional)
Recipes can run on schedule (hourly / daily / weekly) to keep the output dataset fresh.
- Recipes replaced Dataflows for new CRM Analytics development. Older orgs may have both — migrating Dataflows to Recipes is a manual rebuild.
- Heavy Recipes can take long to run. Joins across multi-million-row datasets can take 30+ minutes — schedule for off-peak times.
- Output dataset permissions are configured at the output level. Without proper Row-Level Security predicates, the dataset may expose data the source restricted — audit before publishing.
How organizations use Recipe
Maintains a library of production Recipes for common prep patterns (cohort builds, hierarchy flattening, period-over-period joins) so every new dashboard starts from a shared dataset.
Uses Recipes to combine Salesforce data with external sources (web analytics, ad platform exports) into unified datasets that power cross-channel attribution.
Applies engineering discipline to Recipe design - naming standards, description fields, change review - so analysts can safely modify each other's pipelines.
Trust & references
Test your knowledge
Q1. What is a Recipe in Tableau CRM?
Q2. What operations can recipes do?
Q3. Why invest in recipe quality?
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