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Ingest payments data from your Stripe account to Experience Platform using the user interface
Read the following tutorial to learn how to ingest payments data from your Stripe account to Adobe Experience Platform using the user interface.
Get started
This tutorial requires a working understanding of the following components of Experience Platform:
-
Experience Data Model (XDM) System: The standardized framework by which Experience Platform organizes customer experience data.
- Basics of schema composition: Learn about the basic building blocks of XDM schemas, including key principles and best practices in schema composition.
- Schema Editor tutorial: Learn how to create custom schemas using the Schema Editor UI.
-
Real-Time Customer Profile: Provides a unified, real-time consumer profile based on aggregated data from multiple sources.
Authentication
Read the Stripe overview for information on how to retrieve your authentication credentials.
Connect your Stripe account connect
In the Platform UI, select Sources from the left navigation to access the Sources workspace. You can select the appropriate category from the catalog on the left-hand side of your screen. Alternatively, you can find the specific source you wish to work with using the search option.
Under the Payments category, select Stripe, and then select Set up.
The Connect Stripe account page appears. On this page, you can either use new or existing credentials.
To create a new account, select New account and provide a name, an optional description, and your credentials.
When finished, select Connect to source and then allow some time for the new connection to establish.
| table 0-row-2 1-row-2 | |
|---|---|
| Credential | Description |
| Access token | Your Stripe access token. For information on how to retrieve your access token, read the Stripe authentication guide. |
To use an existing account, select Existing account and then select the account that you want to use from the existing account catalog.
Select Next to proceed.
Select data select-data
Now that you have access to your account, you must identify the appropriate path to the Stripe data that you want to ingest. Select Resource path and then select the endpoint from where you want to ingest data from. The available Stripe endpoints are:
- Charges
- Subscriptions
- Refunds
- Balance Transactions
- Customers
- Prices
Once your endpoint is selected, the interface updates into a preview screen, displaying the data structure of the Stripe endpoint that you selected. Select Next to proceed.
Provide dataset and dataflow details provide-dataset-and-dataflow-details
Next, you must provide information on your dataset and your dataflow.
Dataset details dataset-details
A dataset is a storage and management construct for a collection of data, typically a table, that contains a schema (columns) and fields (rows). Data that is successfully ingested into Experience Platform is stored within the data lake as datasets. During this step, you can create a new dataset or use an existing dataset.
To use a new dataset, select New dataset and then provide a name, and an optional description for your dataset. You must also select an Experience Data Model (XDM) schema that your dataset adheres to.
| table 0-row-2 1-row-2 2-row-2 3-row-2 | |
|---|---|
| New dataset details | Description |
| Output dataset name | The name of your new dataset. |
| Description | (Optional) A brief explanation of the new dataset. |
| Schema | A dropdown list of schemas that exist in your organization. You can also create your own schema prior to the source configuration process. For more information, read the guide on creating an XDM schema in the UI. |
If you already have an existing dataset, select Existing dataset and then use the Advanced search option to view a window of all datasets in your organization, including their respective details, such as whether they are enabled for ingestion to Real-Time Customer Profile or not.
If your dataset is enabled for Real-Time Customer Profile, then during this step, you can toggle Profile dataset to enable your data for Profile-ingestion. You can also use this step to enable Error diagnostics and Partial ingestion.
- Error diagnostics: Select Error diagnostics to instruct the source to produce error diagnostics that you can later reference when monitoring your dataset activity and dataflow status.
- Partial ingestion: Partial batch ingestion is the ability to ingest data containing errors, up to a certain configurable threshold. This feature allows you to successfully ingest all of your accurate data into Experience Platform, while all of your incorrect data is batched separately with information on why it is invalid.
Dataflow details dataflow-details
Once your dataset is configured, you must then provide details on your dataflow, including a name, an optional description, and alert configurations.
Experience Platform can produce event-based alerts that users can subscribe to. These options all require a running dataflow to trigger them. For more information, read the alerts overview
- Sources Dataflow Run Start: Select this alert to receive a notification when your dataflow run begins.
- Sources Dataflow Run Success: Select this alert to receive a notification if your dataflow ends without any errors.
- Sources Dataflow Run Failure: Select this alert to receive a notification if your dataflow run ends with any errors.
When finished, select Next to proceed.
Map fields to an XDM schema mapping
The Mapping step appears. Use the mapping interface to map your source data to the appropriate schema fields before ingesting that data into Experience Platform. For an extensive guide on how to use the mapping interface, read the Data Prep UI guide for more information.
Configure ingestion schedule scheduling
Next, use the scheduling interface to create an ingestion schedule for your dataflow.
Select the frequency dropdown to configure your dataflow’s ingestion frequency.
You can also select the calendar icon and use a pop-up calendar to configure your ingestion start time.
Configure frequency to indicate how often the dataflow should run. You can set your frequency to:
- Once: Set your frequency to
onceto create a one-time ingestion. Configurations for interval and backfill are unavailable when creating a one-time ingestion dataflow. By default, the scheduling frequency is set to once. - Minute: Set your frequency to
minuteto schedule your dataflow to ingest data on a per-minute basis. - Hour:Set your frequency to
hourto schedule your dataflow to ingest data on a per-hour basis. - Day: Set your frequency to
dayto schedule your dataflow to ingest data on a per-day basis. - Week: Set your frequency to
weekto schedule your dataflow to ingest data on a per-week basis.
Once you have configured your dataflow’s ingestion schedule, select Next.
Review your dataflow
The final step in the dataflow creation process is to review your dataflow before executing it. Use the Review step to review the details of your new dataflow before it runs. Details are grouped in the following categories:
- Connection: Shows the source type, the relevant path of the chosen source file, and the number 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, select Finish and allow some time for the dataflow to be created.
Next steps
By following this tutorial, you have successfully created a dataflow to bring payments data from your Stripe source to Experience Platform. For additional resources, visit the documentation outlined below.
Monitor your dataflow
Once your dataflow has been created, you can monitor the data that is being ingested through it to view information on ingestion rates, success, and errors. For more information on how to monitor dataflow, visit the tutorial on monitoring accounts and dataflows in the UI.
Update your dataflow
To update configurations for your dataflows scheduling, mapping, and general information, visit the tutorial on updating sources dataflows in the UI.
Delete your dataflow
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, visit the tutorial on deleting dataflows in the UI.