surfaces visual representations of your data for individual columns and the entire dataset. These visual profiles enable you to make quick assessments of problems, unusual patterns, and required changes to your data, and they are available for use throughout the development of your 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 provided below.
For more background information, see Overview of Visual Profiling.
Counts on the rows, columns, and data types in the current sample are displayed at the bottom of the page in the status bar.
Tip: Sample counts are used for profiling when in the Transformer page. When a visual profile is generated as part of your job, the counts are taken from the entire dataset.
Tip: 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 application, which can be useful for seeing how your dataset has changed during development. For more information, see Profile Your Source Data.
The Transformer page provides multiple mechanisms through the data grid for profiling your dataset.
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 (black) values in the column, when matched against the column's data type. In the above image, the data type is set to URL.
Tip: Click the missing or mismatched values in a column's data quality bar. You are prompted with suggestions of transforms to fix or remove these values.
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.
ISNULLfunction to identify null values in a column, which appear among the category of missing values. See Manage Null Values.
See Data Grid Panel.
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 Column Details from the drop-down for the specific column in the data grid.
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.
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.
When you execute your job, you can 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.
Click the Profile Results checkbox.
When the job finishes from the context menu in the Jobs tab, click View Results.