At the top of the Transformer page, the type of the current sample is displayed next to the dataset name. To open the Samples panel, click the current sample indicator:

Click the current sample button.

In the example above, you can see that the current sample is a Random sample.

Initial Data: The sample is taken from the first set of rows in the first file or table that is part of the dataset. 

• In some cases, the Initial Data sample is the entire dataset. 

  Tip: For purposes of loading the data, the initial data sample is generated and displayed at first. For a better representation of the entire dataset, you should create a new sample.

• In other cases, the Initial Data sample is generated from a collection of files.
• If the recipe is a child recipe, then the Initial Data sample indicates the selected sample of the parent recipe.
• For more information on this special sampling type, see Overview of Sampling.

To create a new sample, click Collect a new sample.

The Samples panel is displayed on the right side of the screen:

To review all samples that you have created, see Sample Jobs Page.

Samples Panel

Current sample:

At the top of the panel, you can review the currently loaded sample. Each user has his own active sample on a dataset.

• Initial Data: By default, the application loads the first N rows of the dataset as the initial data sample when the Transformer page is opened. The number of rows depends on column count, data density, and other factors. If the dataset is small enough, the full dataset is used. 

  NOTE: By default, samples may be up to 10 MB in size or may be limited based on the maximum number of files that can be scanned. For datasets smaller than this limit, the entire dataset is loaded. See Overview of Sampling.

• Click the link in the current sample card to see the list of all available samples.

  Tip: To change the name of a sample, click its card in the list of all available. Then, click the Edit icon.

New samples:

Below the current sample, you can review the available options for creating new samples. Each type of sample reflects a different method of collection.

• To collect a new sample, click the appropriate sample card. See below.
  
  

  Tip: A sample execution is a type of job. Any issues related to the execution of a sampling can be reviewed through the job logs.

• After a sample is created, you can load it at any time, as long as it is still valid. Next to a collected sample, click Load sample.
• For more information on sampling methods, see Overview of Sampling.

Status bar:

At the bottom of the Transformer page, you can review the number of rows and columns and count of data types in the currently displayed sample.

Collect new sample

When a new sample is collected, it is gathered based on the current location in the recipe when the sample is gathered. So, if the recipe contains steps that join in other datasets, those joins are performed to bring together the data from which the sample is executed. 

Collect new sample panel

Steps:

• In the Samples panel, select the type of sample to create. For more information on sample types, see Overview of Sampling.

• In the Collect new sample panel, specify the following parameters, some of which may not be required for your sampling method:

• Choose a sampling method: Select or enter the type of sample. If you already selected a sampling method, this value is pre-populated for you.

• Name: You can enter a new name of the sample as needed. 

  Tip: Naming your samples can assist in tracking them later. For example, you might choose to add a date stamp to the name to track when you captured the sample.

• Scan Type: (Does not apply to all sampling methods) Types of scans:
  • Quick - performs a random scan of the dataset to extract the appropriate number of rows for the sample.
  • Full - gathers the sample from the entire dataset. Depending on the size of the dataset, this method can take a while.

• Column or columns: (Stratified, Cluster-based) Name of the column from which to gather values to evaluate (Anomaly-based) Specify the name or names of one or more columns containing the anomalies to include in your sample. Multiple columns can be specified by comma-separated values. A column range can be specified using the tilde (~) character.

• Condition: (Filter-based, Stratified, Cluster-based, Anomaly-based) Filter the sample based on a specified condition. For example:
  

  invoiceDate > 90

• Anomaly type:(Anomaly-based) Select the type of anomalous values to include in your sample: invalid, missing, or both types.
• Variable overrides: If one or more variables is associated with your dataset, you can define the value overrides to be applied when the sample is executed. 
  • You can use these overrides to sample data from different source files in your dataset with parameters. 
  • A variable can have an empty value.
  • For more information, see Overview of Parameterization.
• To begin collecting the sample, click Collect.
• You can continue working while the sample is collected. When the sample is available, a status message is displayed in the Transformer page.
• You can click Load Sample in the Samples panel to begin using it.

Collected samples

In the Collected samples panel, you can review the available and unavailable samples. If applicable, you can review the variable override values that were applied during the sampling.

To use one of the available samples, click Load. The sample is loaded in the data grid. For more information, see Generate a Sample.