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Configure a dataflow for a CRM connector in the UI

A dataflow is a scheduled task that retrieves and ingests data from a source to a Platform dataset. This tutorial provides steps to configure a new dataflow using your CRM base connector.

Getting started

This tutorial requires a working understanding of the following components of Adobe Experience Platform:
Additionally, this tutorial requires that you have already created a CRM connector. A list of tutorials for creating different CRM connectors in the UI can be found in the source connectors overview .

Select data

After creating your CRM connector, 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 click 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. Next, 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 .
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:
Field
Description
Frequency
Selectable frequencies include Minute, Hour, Day, and Week.
Interval
An integer that sets the interval for the selected frequency.
Start time
A UTC timestamp for which the very first ingestion will occur.
Backfill
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 Name flow step appears, where you must provide a name and an optional description for the dataflow. Click 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 details : Shows the source type, the relevant path of the chosen source file, and the amount of columns within that source file.
  • Mapping details : Shows which dataset the source data is being ingested into, including the schema that the dataset adheres to.
  • Schedule details : 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 your dataflow

Once your dataflow has been created, you can monitor the data that is being ingested through it. Follow the steps below to access a dataflow's dataset monitor.
Within the Sources workspace, select the CRM source you wish to view under the CRM category. Select Connect Source to launch the authentication interface. To view an existing dataflow, select Existing account and select the account you wish to access.
The Source activity screen appears. From here, click the name of a dataset whose activity you want to monitor.
The Dataset activity screen appears. This page displays the rate of messages being consumed in the form of a graph.
For more information on monitoring datasets and ingestion, refer to the tutorial on monitoring streaming dataflows .

Next steps

By following this tutorial, you have successfully created a dataflow to bring in data from a CRM 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:

Appendix

The following sections provide additional information for working with source connectors.

Disable a dataflow

When a dataflow is created, it immediately becomes active and ingests data according to the schedule it was given. You can disable an active dataflow at any time by following the instructions below.
Within the authenticaton screen, select the name of the base connection that's associated with the dataflow you wish to disable.
The Source activity page appears. Select the active dataflow from the list to open its Properties column on the right-hand side of the screen, which 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-Customer Profile data, see the tutorial on Profile population .