Flow Structure and Objects
Within Trifacta®, 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 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.
- Sharing with other users
Execution of pre-configured jobs
- Creating references between recipes and external flows
Data that is imported to the platform is referenced as an imported dataset. An imported dataset is simply a reference to the original data; the data does not exist 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 or stored within Trifacta.
- An imported dataset can be referenced in recipes.
- Imported datasets are created through the Import Data Page.
- 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 sequence 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 and turned into commands that can be executed against data.
- 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.
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). 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.
Working with recipes
Recipes are edited in the Transformer page, which provides multiple methods for quickly selecting and building recipe steps.
Samples: Within the Transformer page, you build the steps of your recipe against a sample of the dataset.
- 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:
- 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 Details Page.
- For more information on the process, see Running Job Basics.
A connection is a configuration object that provides a personal or global integration to an external datastore. Reading data from remote sources and writing results are managed through connections.
- Connections are not associated with individual datasets or flows.
- Connections are not reflected in the above diagram.
- Most connections can be created by individual users and shared as-needed.
- Depending on the datastore, connections can be read-only, write-only, or both.
- For more information, see Connections Page.
You can associate a schedule with a flow. A schedule is a combination of one or more triggers and the outputs that are generated from them.
NOTE: A flow can have only one schedule associated with it.
- A trigger is a scheduled time of execution. When a trigger's time occurs, all of the scheduled output destinations are queued for generation.
- A schedule can have multiple triggers associated with it. Therefore, a flow can be scheduled for execution at multiple intervals.
- A scheduled destination is an output associated with a recipe. This output is generated only when the schedule for the flow is triggered.
- A scheduled destination is not tied to a specific trigger. When a trigger occurs, all scheduled destinations in the flow are generated.
- A scheduled destination generates one or more publishing actions (outputs) from the recipe when triggered.
- A recipe can have only one scheduled destination.
- Each recipe in a flow can have a scheduled destination.
- If a flow has a trigger but no scheduled destination, nothing is generated at trigger time.
Below, you can see the object hierarchy within a schedule.
For more information, see Overview of Automator.
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.
- A trigger for a plan is the schedule for its execution.
- A task is an executable action that is taken as part of a plan's sequence. For example, task #1 could be to execute a flow that imports all of your source data. Task #2 executes the flow that cleans and combines that data.
- A snapshot is a frozen image of the plan. This snapshot of the plan is used plan runs.
For more information, see Overview of Operationalization.
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