The Marketo Engage source in Adobe Experience Platform is currently in beta. The documentation and functionality are subject to change.
This tutorial provides steps for creating a Marketo Engage (hereinafter referred to as “Marketo”) source connector in the UI to bring B2B data into Adobe Experience Platform.
This tutorial requires a working understanding of the following components of Adobe Experience Platform:
In order to access your Marketo account on Platform, you must provide the following values:
||The Munchkin ID is the unique identifier for a specific Marketo instance.|
||The unique client ID of your Marketo instance.|
||The unique client secret of your Marketo instance.|
For more information on acquiring these values, refer to the Marketo authentication guide.
Once you have gathered your required credentials, you can follow the steps in the next section.
In the Platform UI, select Sources from the left navigation bar to access the Sources workspace. The Catalog screen displays a variety of sources for which you can create an account with.
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 bar.
Under the Adobe applications category, select Marketo Engage. Then, select Add data to create a new Marketo dataflow.
The Connect to Marketo Engage page appears. On this page, you can either use a new account or access an existing account.
If you are creating a new account, select New account. On the input form that appears, provide an account name, an optional description, and your Marketo authentication credentials. When finished, select Connect to source and then allow some time for the new connection to establish.
To create a dataflow with an existing account, select Existing account and then select the Marketo account you want to use. Select Next to proceed.
After creating your Marketo account, the next step provides an interface for you to explore Marketo datasets.
The left half of the interface is a directory browser, displaying the 10 Marketo datasets. A fully-functioning Marketo source connection requires the ingestion of the nine different datasets. If you are also using the Marketo account-based marketing (ABM) feature, then you must also create a 10th dataflow to ingest the Named Accounts dataset.
For the purposes of brevity, the following tutorial uses Named Accounts as an example, but the steps outlined below apply to any of the 10 Marketo datasets.
Select the dataset you wish to ingest first, then select Next.
The Mapping step appears, providing an interface to map Marketo schemas to Platform.
Choose a dataset for inbound data to be ingested into. You can either use an existing dataset or create a new dataset.
To ingest data into an existing dataset, select Existing dataset, then select the dataset icon.
The Select dataset dialog appears. Find the dataset with the appropriate schema you wish to use, select it, then select Confirm.
To ingest data into a new dataset, select New dataset and enter a name and description for the dataset in the fields provided.
You can search for a schema by entering its name in the Select schema search bar. You can also select the dropdown icon to see a list of existing schemas. Alternatively, you can select Advanced search to access page of existing schemas including their respective details.
Toggle the Profile dataset button to enable your target dataset for Profile, allowing 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.
Once you have selected a schema, scroll down to view the mapping dialog to start mapping your Marketo dataset fields to their appropriate target XDM fields.
Each Marketo dataset has its own specific mapping rules to follow. See the following for more information on how to map Marketo datasets to XDM:
Select Preview data to see mapping results based on your selected dataset.
The Preview popover provides you an interface to explore mapping results of up to 100 rows of sample data from the selected dataset.
Once your source fields are mapped to the appropriate target fields, select Close.
The Dataflow detail step appears, allowing you to provide a name and a brief description about your new dataflow.
Enable the Error diagnostics toggle to allow for detailed error message generation for newly ingested batches, which you can download using the API. For more information, see the tutorial on retrieving data ingestion error diagnostics.
The Marketo connector uses batch ingestion to ingest all historical records and uses streaming ingestion for real-time updates. This allows the connector to continue streaming while ingesting any erroneous records. Enable the Partial ingestion toggle and then set the Error threshold % to maximum to prevent the dataflow from failing.
Partial ingestion provides the ability to ingest data containing errors up to a certain threshold. For more information, see the partial batch ingestion overview.
Once you have provided your dataflow details and set your error threshold to max, select Next.
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 dataflows, see the tutorial on monitoring dataflows in the UI.
Custom attributes in datasets cannot be retroactively hidden or removed. If you want to hide or remove a custom attribute from an existing dataset, then you must create a new dataset without this custom attribute, a new XDM schema, and configure a new dataflow for the new dataset that you create. You must also disable or delete the original dataflow that consists of the dataset with the custom attribute you want to hide or remove.
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 in Marketo data. 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: