Data prep expands on the intuitive visual interface that lets you
point and click your way to build recipes.
You can build recipes that clean, prepare and
enrich your source data before you load it into a target.
For example, to gain insights about global sales, this sample recipe
combines sales opportunities from Africa, APAC and North America.
Cleans and enriches the data, and then loads the results into a data set,
to learn about the components of a recipe, let's take a deeper look.
This recipe appears as a graphic, the graphics shows where data comes from at
a glance, how the source data flows through the recipe.
And changes at each stage and where the results are output,
each icon in the graph is called a node.
You can add different node types,
an input node specifies the source data for the recipe.
Each input node pulls data from specified fields of either an existing data set or
a connected object.
A connected object provides access to data and a Salesforce or
an external object, such as the Salesforce account or a Google BigQuery object.
You can add multiple input nodes to pull data from multiple source objects.
When you first create a recipe, you select the source data to use as input for
the recipe.
Each source object appears as a separate input node in the graph,
you can add more source data later using the Select Data button.
A filter node filters rose from the recipe,
take a look at the first filter node, the interface splits into two sections.
The graph area up top, with details below,
on the left side of the details area, you can see the node details.
In this case, you can see how this filter node is defined, this node includes only
rows from the opportunities Africa source they have a customer account type.
And append node combines rows from multiple data
sources by stacking the rows.
The first append stacks opportunities from Africa with those from APAC,
the second append as the North America opportunities to the results.
A transform node can include multiple transformations that clean and
enrich data, like trimming blank spaces, predicting missing values.
Detecting the sentiment of text, and using formulas to calculate new values,
you can also use transformations to convert column data types.
To and from dates, dimensions and measures,
this Transform node has multiple transformations.
Each transformation in the left panel the details area is called a step.
These formula steps convert text to lowercase,
concatenate two fields and replace data values.
The drop column step drops columns from the recipe that were only needed as input
for the calculations.
Dropped columns don't appear in the final output, to add a transformation,
select one or more columns.
And then select the transformation from the transform toolbar,
the transformations in the toolbar vary based on the data type.
And number of selected columns,
a join node joins more columns from a related data source to the recipe.
This join uses a left join to add related columns from the contact
information source with the opportunities in the recipe.
An aggregate node rolls up data to a higher level, for example,
this aggregate node groups opportunity records by account.
To create a data set that provides key opportunity metrics for each account,
this node calculates the total opportunity amount, average opportunity amount.
And count of opportunities for each account, an output node identifies
the target where you want to write the final results of the recipe.
You can write to one or more data sets or external objects,
such as a data lake and Amazon S3 or snowflake.
This recipe writes the results to a target data set named aggregates by account.
To learn more, check out this resource or
join us on the trail@trailblazer.salesforce.com