Speaker 1: To get the right data into your data set,
test your recipes as you build them.
In Data Prep,
use the preview feature to review the results at every step of the way.
You can use preview to verify that you've included the right rows,
you've included the right columns and applied the right column data types.
All nodes and transformations produce the expected results.
Verify rows, to show how this works let's start by looking at a sample recipe.
To preview rows pulled from a source, select the input node in the graph.
For example, look at the opportunities Africa input node,
the preview tab shows rows pulled from the opportunities Africa data source.
It can show up to 5000 rows,
if the results contain unnecessary rows, apply a filter to remove them.
No need to process rows that can be ignored, verify the columns and
column types.
To see how this works,
let's preview the resulting columns from the joint transformation.
This joint pulls data from the opportunities Africa and
contact information data sets.
Here you can preview columns pulled from both datasets,
if you want to know which source each column comes from, click the columns tab.
It shows the label, API name, type, and source, for
calculated fields, the sources derived field.
Use the columns tab to determine whether to add columns or
drop unnecessary ones from the recipe.
You can also verify the data type of each column
using the type column in the columns tab.
Remember that data types determine how the column can be used in queries and charts.
For instance, you can perform math calculations on major columns, and
you can filter and group by dimension and date columns.
To identify the data type of a column on the preview tab,
see the icon in the column header.
Einstein Analytics uses the following icons to identify dimension, date and
measure data types, the dimension icon, the date icon, the measure icon.
Verify node and transformation results, you can also preview the actual data so
you can verify that a node or
transformation produces the expected results.
For example, select the transform node and
then the first transformation step to see the transformations results.
This formula transformation converts the values of the industry column to lowercase
and stores them in a new column named industry formula.
Scroll over to find the new industry formula column,
the values are indeed lowercase.
To preview the results of the next transformation step,
select it, the preview updates and shows the results up to the selected step.
It's important that you preview the data to ensure that it's clean enough to give
you accurate insights.
In the data integration space, there's a saying garbage in, garbage out.
If your data has poor quality, your results can be skewed and misleading, for
instance, look back at the joint node.
The two sources are joined based on joint keys account owner, and
customer full name, the account owner column contains these values.
Speaker 1: Next, check the customer Full Name column from the other source.
Oops, this column doesn't have any values,
without values to match there will be no joints.
That's also why there aren't any values for
columns coming from the second data source.
If you encounter data quality issues as you preview the data,
know that you can use transformations.
Like to predict missing values and
replace transformations to clean your data before generating your datasets.
To learn more, check out this resource or
join us on the trail @trailblazer.salesforce.com