Configure a dataflow for a cloud storage batch 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 cloud storage base connector.
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 cloud storage connector. A list of tutorials for creating different cloud storage connectors in the UI can be found in the source connectors overview .
Supported file formats
Experience Platform supports the following file formats to be ingested from external storages:
- Delimiter-separated values (DSV): Support for DSV-formatted data files is currently limited to comma-separated values. The value of field headers within DSV formatted files must only consist of alphanumeric characters and underscores. Support for general DSV files will be provided in the future.
- Apache Parquet: Parquet-formatted data files must be XDM-compliant.
After creating your cloud storage connector, the Select data step appears, providing an interactive interface for you to explore your cloud storage 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.
Clicking a listed folder allows you to traverse the folder hierarchy into deeper folders. Once you have a compatible file or folder selected, the Select data format dropdown appears, where you can choose a format to display the data in the preview window.
Once the preview window populates, you can click Next to upload all files within the selected folder. If you want to upload to a specific file, select that file from the listing before clicking Next .
Supported file formats include CSV, JSON, and Parquet. JSON and Parquet files must be XDM-compliant.
Map data fields to an XDM schema
The Mapping step appears, providing an interactive interface to map the source data to a Platform dataset. Source files formatted in JSON or Parquet must be XDM-compliant and do not require you to manually configure the mapping. CSV files, conversely, require you to explicitly configure the mapping, but allow you to pick which source data fields to map.
Choose a dataset for inbound data to be ingested into. You can either use an existing dataset or create a new one.
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:
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 Name flow step appears, allowing you to name and give a brief description about your new dataflow.
Provide values for the dataflow and click Next .
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:
- Source details : Shows the source type, the relevant path of the chosen source file, and the amount of columns within that source file.
- Target 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 cloud storage dataflow has been created, you can monitor the data that is being ingested through it. For more information on monitoring datasets, see the tutorial on monitoring streaming dataflows .
By following this tutorial, you have successfully created a dataflow to bring in data from an external cloud storage, 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 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 Sources workspace, click the Browse tab. Next, click the name of the account that's associated the active 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 .