A dataflow is a scheduled task that retrieves and ingests data from a source to a dataset in Adobe Experience Platform. This tutorial provides steps on how to create a dataflow for a protocols source using the Platform UI.
In order to create a dataflow, you must already have an authenticated account with a protocols source. A list of tutorials for creating different protocols source accounts in the UI can be found in the sources overview.
This tutorial requires a working understanding of the following components of Platform:
After creating your protocols source account, the Add data step appears, providing an interface for you to explore your protocols source account’s table hierarchy.
The search source data option is available to all table-based sources excluding the Adobe Analytics, Amazon Kinesis, and Azure Event Hubs.
Once you find the source data, select the table, then select Next.
The Dataflow detail page allows you to select whether you want to use an existing dataset or a new dataset. During this process, you can also configure settings for Profile dataset, Error diagnostics, Partial ingestion, and Alerts.
To ingest data into an existing dataset, select Existing dataset. You can either retrieve an existing dataset using the Advanced search option or by scrolling through the list of existing datasets in the dropdown menu. Once you have selected a dataset, provide a name and a description for your dataflow.
To ingest into a new dataset, select New dataset and then provide an output dataset name and an optional description. Next, select a schema to map to using the Advanced search option or by scrolling through the list of existing schemas in the dropdown menu. Once you have selected a schema, provide a name and a description for your dataflow.
Next, select the Profile dataset toggle to enable your dataset for Profile. This allows you to create a holistic view of an entity’s attributes and behaviors. Data from all Profile-enabled datasets will be included in Profile and changes are applied when you save your dataflow.
Error diagnostics enables detailed error message generation for any erroneous records that occur in your dataflow, while Partial ingestion allows you to ingest data containing errors, up to a certain threshold that you manually define. See the partial batch ingestion overview for more information.
You can enable alerts to receive notifications on the status of your dataflow. Select an alert from the list to subscribe to receive notifications on the status of your dataflow. For more information on alerts, see the guide on subscribing to sources alerts using the UI.
When you are finished providing details to your dataflow, select Next.
The Mapping step appears, providing you with an interface to map the source fields from your source schema to their appropriate target XDM fields in the target schema.
Platform provides intelligent recommendations for auto-mapped fields based on the target schema or dataset that you selected. You can manually adjust mapping rules to suit your use cases. Based on your needs, you can choose to map fields directly, or use data prep functions to transform source data to derive computed or calculated values. For comprehensive steps on using the mapper interface and calculated fields, see the Data Prep UI guide.
Once your source data is successfully mapped, select Next.
The Scheduling step appears, allowing you to configure an ingestion schedule to automatically ingest the selected source data using the configured mappings. By default, scheduling is set to
Once. To adjust your ingestion frequency, select Frequency and then select an option from the dropdown menu.
Interval and backfill are not visible during a one-time ingestion.
If you set your ingestion frequency to
Week, then you must set an interval to establish a set time frame between every ingestion. For example, an ingestion frequency set to
Day and an interval set to
15 means that your dataflow is scheduled to ingest data every 15 days.
During this step, you can also enable backfill and define a column for the incremental ingestion of data. Backfill is used to ingest historical data, while the column you define for incremental ingestion allows new data to be differentiated from existing data.
See the table below for more information on scheduling configurations.
|Frequency||The frequency in which an ingestion happens. Selectable frequencies include
|Interval||An integer that sets the interval for the selected frequency. The interval’s value should be a non-zero integer and should be set to greater than or equal to 15.|
|Start time||A UTC timestamp indicating when the very first ingestion is set to occur. Start time must be greater than or equal to your current UTC time.|
|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.|
|Load incremental data by||An option with a filtered set of source schema fields of type, date, or time. This field is used to differentiate between new and existing data. Incremental data will be ingested based on the timestamp of selected column.|
The Review step appears, allowing you to review your new dataflow before it is created. Details are grouped within the following categories:
Once you have reviewed your dataflow, select Finish and allow some time for the dataflow to be created.
Once your dataflow has been created, you can monitor the data that is being ingested through it to see information on ingestion rates, success, and errors. For more information on how to monitor dataflow, see the tutorial on monitoring accounts and dataflows in the UI.
You can delete dataflows that are no longer necessary or were incorrectly created using the Delete function available in the Dataflows workspace. For more information on how to delete dataflows, see the tutorial on deleting dataflows in the UI.
By following this tutorial, you have successfully created a dataflow to bring data from your protocols source to Platform. 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 Platform UI shown in the following video is out-of-date. Please refer to the documentation above for the latest UI screenshots and functionality.