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D s product
 surfaces visual representations of your data for individual columns and the entire dataset



Tip: Visual profiling is especially important in recipe development. When you identify something of interest, you can select the visual representation of it, and the platform prompts you with a set of suggested transforms to add to your recipe. Examples are below.

For more background information, see Overview of Visual Profiling.

Profile Source Data


Tip: and provides mechanisms for taking immediate action on issues in the data.

Profiling Source Data

When you first load your dataset into the application, you might want to run a job to profile your dataset before you build your recipe. The generated results and profile are accessible through the


Job Details page in the 

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. This profile of your source can be useful


later in seeing how your dataset has changed during development.


Profiling in the Application

In When you identify something of interest in the 

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, there are a number of features that provide visual information on the status of individual columns, their data, and the overall dataset, you can select the visual representation of it, and the platform prompts you with a set of suggested transforms to add to your recipe. These visual profiles enable you to make quick assessments of problems, unusual patterns, and required changes to your data.

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Status Bar

Counts on the The number of rows, columns, and data types in the current sample are displayed at the bottom of the page in the status bar.





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Column Header

The top of each column contains a data quality bar, which identifies the valid, mismatched, and missing values in the column when compared against the specified data type, and column histogram, which identifies the range of values in the column. 


Below the name of the column, the multi-colored band indicates the valid (green), mismatched (red), and missing (gray) values in the column, when matched against the column's data type. In the above image, the data type is set to URL.


Click the missing or mismatched values in a column's data quality bar. You are prompted with suggestions of transformations to fix or remove these values.

Column Histogram

Each column includes a histogram of the values in the column. In the above image, there are 402 different values in the column, and you can see how some values appear more frequently than others.


  • In the column histogram, you can select a column value and drag to select a range of values for suggestions on transformations. 
  • Null values are a special case of missing values. You can use the ISNULL function to identify null values in a column, which appear among the category of missing values. See Manage Null Values.
  • When you select one or more values in the column histogram, you can see the corresponding values for the row values in the histograms for other columns.


The bar chart at the top of each column in the Transformer page is called a histogram. Each column histogram displays the count of each detected value in the column (for string data) or the count of values within a numeric range (for number data). You can use this histogram to identify unusual values or outlier values, which can be corrected or removed.

Column Details - statistics and outliers

In the Column Details window, you can review key statistical information on the values in a column. Displayed statistics are based on the column's data type. To explore the details for a column's data, select Select Column Details from the drop-down for the specific column in the data grid

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Column Details

For the selected column, you can review key statistics depending on the data type. The above image shows statistics that apply to the URL data type, which is a variation on String type. 


  • Make a selection from the lists of top, mismatched, and other value lists to be prompted for a set of suggestions for how to modify the selected rows.
  • Transform suggestions are updated based on your selection. 
  • Click the missing values in the data quality bar to prompt for suggestions to address those values in the column.

See Column Details Panel.

Column Browser - profiles across columns

In the column browser, you can view visual histograms for each column in the dataset and make selections to identify correlations between values in multiple columns. To open the column browser, click the Columns icon in the Transformer bar.

For more information, see Column Browser Panel.

Profile Jobs


Profiling in Job Results

When you run a  job, you can choose to generate a visual profile of the entire dataset as part of the job. You can use the generated profile to simplify iteration on your recipe. The optional profiling of the results can take extra time to generate. 


  1. In the Transformer page, click Run.
  2. Click the Profile Results checkbox.  

  3. Run the job. 
  4. When the job finishes, click the Job Id link. Then, click the Profile tab in the Job Details page. 

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Visual Profile

This visual profile displays statistics across the entire dataset. Since the data volume of the entire dataset can be quite large, these stats may be approximationsbased on the job results from the Run Job page.

Download visual profile

From the Profiles tab, you can download your job's visual profile to your desktop.See Job Details Page.