A sample is a selection of rows from your dataset, which can be used as the basis for building the transformation steps in your recipe. The automatically creates initial samples of your data whenever you create a new recipe for a dataset and enables you to create additional samples at any time using a variety of sampling techniques.
When you create a new recipe and load it in the Transformer page, the displays the initial sample of the dataset. The initial sample consists of the first X rows of the datasets, where X is determined by the following factors:
These first rows are displayed for you to begin your work in the Transformer page. However, you may begin to run into limitations with this sample. For example, suppose your dataset is organized by date, with earliest dates listed first. There may be significant changes in the data later in the time period that do not appear in the initial sample. You may decide that you need to take a different sample that captures some of these changes.
The Samples panel is displayed.
At the top of the panel, you can review the Current Sample.
Tip: If the current sample indicates Full Data, then the entire dataset is displayed in the data grid. Unless you wish to use a specific sampling technique to filter down your data, sampling may not be useful across the entire dataset.
Depending on your product edition, you may be able to select Quick Scan or Full Scan.
NOTE: This option is not supported in or .
For more information, see Samples Panel.
NOTE: After you generate a sample, all steps in a recipe that occur after the step selected when you generated the sample are executed in browser memory on the sample data and then displayed in the data grid.
The above statement is best explained by example:
|1. Create a new recipe and open it in Transformer page.||The initial sample is generated and displayed.|
|2. Add 3 steps to your recipe.||The 3 new steps are applied to the initial sample in the browser's memory.|
|3. Generate a new random sample.|
The random sample is generated. When you load the sample, it is displayed in the data grid.
|4. Add 25 steps to your recipe.||The 25 new steps are applied to the random sample in the browser's memory.|
|5. Select one of the first 3 steps of your recipe.||The initial sample is loaded and displayed.|
|6. Insert a new step below the current one.||Now, the first 4 steps are displayed using the initial sample.|
Tip: When resources permit, it's a good habit to take a new sample after a few multi-dataset operations or operations that otherwise change the number of rows in your dataset have been added to your recipe.
Samples can become invalid. If you recipe steps change the number of rows or otherwise reshape your dataset using transformations such as pivot or join in the steps leading up to where you took the current sample, your existing sample may no longer be valid.
When the application determines that a sample is invalid:
The application automatically reverts to the last known good sample.
NOTE: Depending on when the last known good sample was generated, this reversion could suddenly force a large number of steps to be processed in the browser's memory.
For more information, see Overview of Sampling.For more information on best practices, see https://community.trifacta.com/s/article/Best-Practices-Managing-Samples-in-Complex-Flows.