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.
Data from the rest of the first file or table or from other files or tables is not included in the data grid.
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.
Tip: You can also open the Samples panel by clicking the Eyedropper icon at the top of the page.
To review all samples that you have created, see Sample Jobs Page.
At the top of the panel, you can review the currently loaded sample. Each user has his own active sample on a dataset.
NOTE: When a new sample is generated, any Sort transformations that have been applied previously must be re-applied. Depending on the type of output, sort order may not be preserved.
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. For datasets smaller than this limit, the entire dataset is loaded.
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.
NOTE: Except for the initial data sample, all samples are generated based on the steps leading up to the location of the cursor in the recipe. If earlier steps are deleted or modified, the collected sample can be invalidated.
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 (
NOTE: If you add recipe steps that change the number of rows in your dataset (or a few other edge case steps), some of your existing samples may no longer be valid. When you execute a join, union, or delete action or edit steps before this action, you may be prompted with the Change Recipe dialog, which includes the following message:
Your change will invalidate some of the currently available samples for this source. The invalid samples will be deactivated.
For more information on the types of transformations that can invalidate samples, see Reshaping Steps.