ÃÛ¶¹ÊÓƵ

[Beta]{class="badge informative"}

Create a Merkury Enterprise Identity Resolution source connection and dataflow in the UI

This tutorial provides steps to create a Merkury Enterprise Identity Resolution source connection and dataflow using the ÃÛ¶¹ÊÓƵ Experience Platform user interface.

Get started

This tutorial requires a working understanding of the following components of Experience Platform:

Gather required credentials

In order to access your bucket on Experience Platform, you need to provide valid values for the following credentials:

For more information on set up for Merkury and other prerequisites, read the Merkury source overview.

Connect your Merkury Account

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 option.

Under the Data partners category, select Merkury and then select Set up.

°Õ³ó±ðÌý Connect to Merkury  page appears. On this page, you can either use new credentials or existing credentials.

Create a new account

If you are using new credentials, select  New account. On the input form that appears, provide a name, an optional description, and your Merkury credentials. When finished, select  Connect to source  and then allow some time for the new connection to establish.

Use an existing account

To use an existing account, select Existing account and then select the Merkury account that you would like to use. Select Next to proceed.

Supported file formats

You can ingest the following file formats with the Merkury source:

Add data

After creating your Merkury account, the Add data step appears, providing an interface for you to explore your Merkury file hierarchy and select the folder or specific file that you want to bring to Experience Platform.

Select the root folder to access your folder hierarchy. From here, you can select a single folder to ingest all files in the folder recursively. When ingesting an entire folder, you must ensure that all files in that folder share the same data format and schema.

Once you have selected a folder, the right interface updates to a preview of the contents and structure of the first file in the selected folder.

During this step, you can make several configurations to your data, before proceeding. First, select Data format and then select the appropriate data format for your file in the dropdown panel that appears.

The following table displays the appropriate data formats for the supported file types:

Select a column delimiter

Ingest compressed files

Provide dataflow details

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 your data to be ingested to Profile, and enable settings like Error diagnostics, Partial ingestion, and Alerts.

Use an existing dataset

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.

Use a new dataset

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.

Enable Profile and error diagnostics

Enable alerts

Map data fields to an XDM schema

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.

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. By default, scheduling is set to Once. To adjust your ingestion frequency, select Frequency and then select an option from the dropdown menu.

If you set your ingestion frequency to Minute, Hour, Day, or 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.

When finished configuring your ingestion schedule, select 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:

Once you have reviewed your dataflow, click 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 batch data from your Merkury 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.