Data flows provide a design experience based on selecting the patterns you apply, starting from source and ending in destination. Unlike strategies and process flows, you do not add components or shapes directly to the data flow, you define patterns by clicking the add icon available when you focus on a shape, and selecting from the instructions you can use to create paths that lead to the destination. As a result, the visual layout of the data flow is determined by the sequence of instructions or execution points and, as you define the properties of each shape, the data flow calculates the connection to the destination.

• Source and destination
• Combine data
• Convert data
• Merge data
• Run strategy

You can toggle the way you visualize the labels of the shapes in data flow canvas by selecting Names or Classes in the data flow toolbar:

• Names: labels display the name of the shape. The name is automatically derived from the way you configure the shape to operate.
• Classes: labels display the class name.

Source

Source is the standard entry point of a data flow. A source defines data you read from through the data flow.

• A data flow contains one primary source. This primary source can originate from a data set, another data flow or represent an abstract set of results:
  • Data set: the entry point is based on the data defined in a data set in the data flow class.
  • Data flow: the entry point is based on another data flow. This data flow can be under the same data flow class or another class.
  • Abstract: results in the data class.
• Secondary sources are added as you start combining data. Secondary data sources can originate from a data set or another data flow.

Compose

The compose pattern allows you to combine data from two sources into a page or page list property. This pattern requires a property to match data between the two sources. The starting data point is the shape where where you start combining data from. Once the compose path is available, you define the secondary source by providing an existing data set or another data flow.

Convert

The convert pattern allows you to take data in one class and put it in another, overwriting identical properties (by name), or explicitly mapping properties that do not have the same name between source and target. This pattern requires a property to match data between the two sources. The data you pass is determined by the starting shape for the convert path. Once the convert path is available, you define its properties by selecting the target class and how to handle property mapping between source and target.

The mapping of properties between source and target can be handled automatically, or you explicitly define how to map source and target properties.

• Automatically means that properties in the source override properties with the same name in the target class.
• Explicit mapping allows you to manage the mapping in cases you want to control which properties result in an overwrite, or you do not have properties with the same name, although they are compatible properties that represent the same data.

Merge

The merge pattern allows you to combine data in the primary and secondary data paths into a single track. This pattern requires the source to be in the same class, conditions to match between primary and secondary sources, and how to handle data mismatches. The starting data point is the shape where you start merging data from. Once the merge path is available, you define the secondary source by providing an existing data set or another data flow.

In cases of data mismatch, you can select with source is leading:

• Primary path: if there are properties with the same name but different values, merge takes the value in the primary source.
• Secondary path: if there are properties with the same name but different values, merge takes the value in the secondary source.

Strategy

The strategy pattern allows you to run a strategy based on combined or not combined data. The data you pass to execute the strategy is determined by the starting shape for the strategy path. Once the strategy path is available, you define which strategy to run and, if applicable, model learning and outcome capture settings.

Model learning and outcome capture settings and related, but exclusive. In a data flow, each strategy can only run in one context: storing adaptive inputs and strategy results, or retrieving them.

• Store adaptive inputs and strategy results for: used for delayed adaptive model learning and issuing response capture at a later date, and applies when the strategy executed by the data flow is designed to issue a decision. Provide an amount of time in days, hours or minutes for persisting the inputs passed to adaptive models and strategy results, assuming a strategy that issues a decision.
• Retrieve previous inputs and results using key: used to retrieve the adaptive inputs and strategy results, and applies when the strategy executed is by the data flow is designed to capture results.

Destination

The destination is the standard output point of a data flow. A destination defines the data point you write to.

• Every data flow defines one or more destinations that output all results, or results based on a given condition.
• The types of data representation you can use in the in a destination are the same as in the source (data set, data flow, and abstract).
• The type of data set determines the write operation performed by the destination: database tables, decision data stores, ADM by using the pxAdaptiveAnalytics data set, IH by using the pxInteractionHistory data set, or a VBD data source.

About Data Flow rules