Configure a dataflow for a payment connector in the UI
A dataflow is a scheduled task that retrieves and ingests data from a source to an Adobe Experience Platform dataset. This tutorial provides steps to configure a new dataflow using your payments account.
This tutorial requires a working understanding of the following components of Adobe Experience Platform:
- Experience Data Model (XDM) System : The standardized framework by which Experience Platform organizes customer experience data.
- Real-time Customer Profile : Provides a unified, real-time consumer profile based on aggregated data from multiple sources.
Additionally, this tutorial requires that you have already created a payments account. A list of tutorials for creating different payment connectors in the UI can be found in the source connectors overview .
After creating your payments account, the Select data step appears, providing an interactive interface for you to explore your file hierarchy.
- The left half of the interface is a directory browser, displaying your server's files and directories.
- The right half of the interface lets you preview up to 100 rows of data from a compatible file.
Select the directory you wish to use, then select Next .
Map data fields to an XDM schema
The Mapping step appears, providing an interactive interface to map the source data to a Platform dataset.
Choose a dataset for inbound data to be ingested into. You can either use an existing dataset or create a new dataset.
Use an existing dataset
To ingest data into an existing dataset, select Use existing dataset , then click the dataset icon.
The Select dataset dialog appears. Find the dataset you you wish to use, select it, then click Continue .
Use a new dataset
To ingest data into a new dataset, select Create new dataset and enter a name and description for the dataset in the fields provided.
During this process, you can also enable Partial ingestion and Error diagnostics . Enabling Partial ingestion provides the ability to ingest data containing errors, up to a certain threshold that you can set. Enabling Error diagnostics provides details on any incorrect data that is batched separately. For more information, see the partial batch ingestion overview .
When finished, click the schema icon.
The Select schema dialog appears. Select the schema you wish to apply to the new dataset, then click Done .
Based on your needs, you can choose to map fields directly, or use mapper functions to transform source data to derive computed or calculated values. For more information on data mapping and mapper functions, refer to the tutorial on mapping CSV data to XDM schema fields .
The Mapping screen also allows you to set Delta column . When the dataset flow is created, you can set any timestamp field as a basis to decide which records to ingest in scheduled incremental ingestions.
Once your source data is mapped, click Next .
Schedule ingestion runs
The Scheduling step appears, allowing you to configure an ingestion schedule to automatically ingest the selected source data using the configured mappings. The following table outlines the different configurable fields for scheduling:
Selectable frequencies include Minute, Hour, Day, and Week.
An integer that sets the interval for the selected frequency.
A UTC timestamp for which the very first ingestion will occur.
A boolean value that determines what data is initially ingested. If Backfill is enabled, all current files in the specified path will be ingested during the first scheduled ingestion. If Backfill is disabled, only the files that are loaded in between the first run of ingestion and the Start time will be ingested. Files loaded prior to Start time will not be ingested.
Dataflows are designed to automatically ingest data on a scheduled basis. If you wish to only ingest once through this workflow, you can do so by configuring the Frequency to "Day" and applying a very large number for the Interval , such as 10000 or similar.
Provide values for the schedule and click Next .
Name your dataflow
The Dataset flow detail step appears, where you must provide a name and an optional description for the dataset flow. Select Next when finished.
Review your dataflow
The Review step appears, allowing you to review your new dataflow before it is created. Details are grouped within the following categories:
- Connection : Shows the source type, the relevant path of the chosen source file, and the amount of columns within that source file.
- Assign dataset & map fields : Shows which dataset the source data is being ingested into, including the schema that the dataset adheres to.
- Scheduling : Shows the active period, frequency, and interval of the ingestion schedule.
Once you have reviewed your dataflow, click Finish and allow some time for the dataflow to be created.
Monitor and delete your dataflow
Once your dataflow has been created, you can monitor the data that is being ingested through it. For more information on how to monitor and delete your dataflow, see the tutorial on monitoring and deleting dataflows .
By following this tutorial, you have successfully created a dataset flow to bring in data from a marketing automation system and gained insight on monitoring datasets. Incoming data can now be used by downstream Platform services such as Real-time Customer Profile and Data Science Workspace. See the following documents for more details:
The following sections provide additional information for working with source connectors.
Disable a dataset flow
When a dataset flow is created, it immediately becomes active and ingests data according to the schedule it was given. You can disable an active dataset flow at any time by following the instructions below.
Within the Dataset Flows screen, select the name of the dataset flow you wish to disable.
The Properties column appears on the right-hand side of the screen. This panel contains an Enabled toggle button. Click the toggle to disable the dataflow. The same toggle can be used to re-enable a dataflow after it has been disabled.
Activate inbound data for Profile population
Inbound data from your source connector can be used towards enriching and populating your Real-time Customer Profile data. For more information on populating your Real-time Customer Profile data, see the tutorial on Profile population .