|Standard job execution||Recipe 1/Job 1|
Results of the job are used to create a new imported dataset (I-Dataset 2). See Job Details Page.
|Create dataset from generated results||Recipe 2/Job 2|
Recipe 2 is created off of I-Dataset 2 and then modified. A job has been specified for it, but the results of the job are unused.
|Chaining datasets||Recipe 3/Job 3|
Recipe 3 is chained off of Recipe 2. The results of running jobs off of Recipe 2 include all of the upstream changes as specified in I-Dataset 1/Recipe1 and I-Dataset 2/Recipe 2.
|Reference dataset||Recipe 4/Job 4||I-Dataset 4 is created as a reference off of Recipe 3. It can have its own recipe, job, destinations, and results.|
Flows are created in the Flows page. See Flows Page.
- A sample is typically a subset of the entire dataset. For smaller datasets, the sample may be the entire dataset.
- As you build or modify your recipe, the results of each modification are immediately reflected in the sampled data. So, you can rapidly iterate on the steps of your recipe within the same interface.
- As needed, you can generate additional samples, which may offer different perspectives on the data.
- See Transform Basics.
Flow parameters: You can specify flow parameters that can be referenced in your recipes. When invoked in a step, a flow parameter replaces its reference with the default string value associated or any override value that you have specified for it. See Overview of Parameterization.Macros: As needed, you can create reusable sequences of steps that can be parameterized for use in other recipes. For more information, see Overview of Macros.
Run Jobs: When you are satisfied with the recipe that you have created in the Transformer page, you can execute a job. A job may be composed of one or more of the following job types:
For more information, see Overview of Automator.
|D s ed|
|D s ed|
A plan is a sequence of triggers and tasks that can be executed across multiple flows. A plan is executed on a snapshot of all objects at the time that the plan is triggered.