Prepare Datasets with Recipes (Generally Available)

Preparing data in Wave Analytics just got a whole lot easier. Dataset recipes are now generally available and come with a slew of new features. Take data in an existing dataset or replication and clean up or remove fields, and add fields from other datasets or replications. In addition, you can now filter rows, add calculations and bucket fields, and edit your recipe steps inline. When you run the recipe, you get a new dataset with just the data you need. And remember, still no JSON required!

Data preparation with recipes was introduced as a beta feature in the Winter ‘17 release, but is now generally available and already turned on for you in Wave.

What Is a Dataset Recipe?

A dataset recipe is a saved set of transformations, or steps, to perform on a specific source dataset or replication. When you run a recipe, it applies the transformations and outputs the results to a new target dataset. You can schedule a recipe to run on a recurring basis to keep your target dataset up to date.

Dataset recipe diagram

Use a recipe to combine data from different sources, and modify field values to ensure consistency in the new dataset you create. You can use the new dataset as a standalone dataset for exploration or dashboard creation, or in your dataflows or other recipes.

Create a Dataset Recipe

You create, run, schedule, and manage your recipes all in one place—the data manager.

Data manager on gear menu

Head to the Prepare tab of the data manager, where you can access your existing recipes and datasets.

Prepare in data manager

Go to any dataset or replication in the data manager to create a recipe for it, or click Create Recipe to select your source data and get started. All your data preparation takes place on a single dataset recipe page, so all the tools you need are close at hand.

Dataset recipe

The recipe preview (1) gives you a real-time preview of your data as you prepare it. If you’re feeling overwhelmed by too many fields or rows, click the Edit recipe preview above the preview to change what you see. As you work on your data, your changes appear as recipe steps in the left pane (2). When you can’t find a field, use the field navigator in the right pane (3) to search for a field. If you see a field that you don’t need in your preview, click the Hide fieldto hide it. You can toggle the field navigator on and off with the Field navigator button button at the top of the page.



The recipe preview displays up to 100 fields. Modify the preview or hide fields to ensure that you can see just the fields you are preparing. When you later create the dataset, you can select all the fields you want for the new dataset.

Prepare the Data

Your data preparation tools are all there for you at the top of the recipe page:

Recipe tool buttons

Let’s say you have a US Leads dataset that you are preparing for use in a marketing dashboard. Here’s what you can do.

Reipe add data button

Add fields from a lookup dataset or replication. Using a merge key field to match rows in your recipe with rows in your lookup, select the lookup fields you want to bring into your recipe.

For example, join a geodata dataset to your leads dataset using the zip code as a merge key.

Recipe add data

Then select the geodata fields.

Recipe select lookup fields


You can add fields from up to 10 datasets or replications. The lookup dataset preview can display up to 100 fields. Modify the preview or hide fields to ensure that you can see just the fields that you need. When you later create the dataset, you can select all the lookup fields you want for the new dataset.

Recipe add filter button

Filter the rows in your recipe.

For example, filter out leads that have a PO box zip code.

Recipe filter
Recipe add bucket button

Add a field to bucket values from a specified dimension or measure field.

For example, add a field to categorize leads according to the number of people in the household.

Recipe bucket field
Recipe add formula button

Add a formula field to calculate new values from measures in your recipe using arithmetic operators and math functions.

For example, calculate how the household size for each lead compares to the national average.

Recipe formula field
Recipe transform button

Transform values in a specified dimension field. You can change case, split values using a delimiter, truncate values, or replace values with new ones.

For example, change category values to match the values that marketing uses.

Recipe transform field

Navigate Your Changes

As you prepare your data, each change you make appears as a recipe step on the left. Don’t worry if you don’t get it right the first time. You can revisit each step to make changes.

Hover and click Action menu button to see options. Go to takes you back and forward to see your data at different steps. Edit lets you make changes. Make changes to formula fields and transformations inline!
Recipe steps menu Recipe got to step Recipe steps inline edit


A recipe can have up to 100 recipe steps.

Create the Dataset

You can save the recipe at any time to come back to it later. When the recipe is complete, click Create Dataset.



A recipe run counts toward your limit of 24 dataflow runs per day. For example, in an org using the Sales Wave app, a user dataflow, and two recipes, there are four separate dataflows. If you schedule each one to run every six hours, you reach the daily limit. Consider this limit when you schedule a recipe.