Flow Structure and Objects
Within Trifacta® Wrangler, the basic unit for organizing your work is the flow. The following diagram illustrates the component objects of a flow and how they are related:
A flow is a container for holding one or more imported datasets, associated recipes and other objects. This container is a means for packaging Trifacta objects for the following types of actions:
Creating relationships between datasets, their recipes, and other datasets.
Execution of pre-configured ad-hoc jobs
- Creating references between recipes and external flows
A flow can be created in an empty state or as a container to hold datasets as you import them.
Data that is imported to the platform is referenced as an imported dataset. An imported dataset is simply a reference to the original data; it is not modified or stored within the platform. An imported dataset can be a reference to a file, multiple files, database table, or other type of data.
NOTE: An imported dataset is a pointer to a source of data. It cannot be modified within Trifacta Wrangler.
- An imported dataset can be referenced in recipes.
- Imported datasets are created through the Import Data Page.
- When the data is first imported, you may optionally include a set of steps to perform initial parsing of the data into rows and columns. These steps may vary depending on the type of source data. See Initial Parsing Steps.
- For more information on the process, see Import Basics.
After you have created an imported dataset, it becomes usable after it has been added to a flow. You can do this as part of the import process or later.
A recipe is a user-defined sequential set of steps that can be applied to transform a dataset.
- A recipe object is created from an imported dataset or another recipe.
- You can create a recipe from a recipe to chain together recipes.
- Recipes are interpreted by
Trifacta Wrangler and turned into commands that can be executed against data. This data can be:
- an imported dataset
- the output of another recipe in the same flow
- a referenced dataset, which is the output from a recipe in a different flow.
- When initially created, a recipe contains no steps. Recipes are augmented and modified using the various visual tools in the Transformer Page.
- For more information on the process, see Transform Basics.
In a flow, the following objects are associated with each recipe, which are described below:
Outputs and Publishing Destinations
Outputs contain one or more publishing destinations, which define the output format, location, and other publishing options that are applied to the results generated from a job run on the recipe.
When you select a recipe's output object in a flow, you can:
- Define the publishing destinations for outputs that are generated when the recipe is executed. Publishing destinations specify output format, location, and other publishing actions. A single recipe can have multiple publishing destinations.
- Run an on-demand job using the specified destinations. The job is immediately queued for execution.
References and Reference Datasets
References allow you to create a reference to the output of the recipe's steps in another dataset. References are not depicted in the above diagram.
When you select a recipe's reference object, you can add it to another flow. This object is then added as a reference dataset in the target flow. A reference dataset is a read-only version of the output data generated from the execution of a recipe's steps.
Working with recipes
Recipes are edited in the Transformer page, which provides multiple methods for quickly selecting and building recipe steps.
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 both of the following job types:
- Transform job: Executes the set of recipe steps that you have defined against your sample(s), generating the transformed set of results across the entire dataset.
- Profile job: Optionally, you can choose to generate a visual profile of the results of your transform job. This visual profile can provide important feedback on data quality and can be a key for further refinement of your recipe.
- When a job completes, you can review the resulting data and identify data that still needs fixing. See Job Results Page.
- For more information on the process, see Running Job Basics.
The following diagram illustrates the flexibility of object relationships within a flow.
|Standard job execution||Recipe 1/Job 1|
Results of the job are used to create a new imported dataset (I-Dataset 2).
|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.
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