Manage connections
Once you have created or edited one or more connections, you can manage them in Connections. Connections let you:
- View all your connections at a glance, including the owner, the sandbox, and when the connections were created and modified.
- Edit a connection.
- Delete a connection.
- Create a data view from a connection.
- View all datasets in a connection.
- Check the status of your connection’s datasets and the status of the ingestion process. For example, when is your data available so that you can start with reporting and analysis in Analysis Workspace.
- Identify any data discrepancies due to misconfiguration. Are you missing any rows? If so, what rows are missing and why? Did you misconfigure connections and cause missing data in Customer Journey Analytics?
- Get insights on the usage of ingested and reportable rows across all your connections.
Connections has two interfaces: List and Usage.
List
The List interface is the default interface for Connections. If not selected, select the List tab to access the interface.
The List interface shows a table of all connections available. You can quickly search for a connection using the Search
The following columns or icons are available in the table.
To view information about Datasets included, Sandbox, Owner, and more, select
A popup window displays details.
Select
One or more links to the datasets that are part of the connection. You can select the dataset hyperlink to view the dataset in the connection. If more datasets are part of the selected connection, select +x more to show a Datasets included panel. This panel shows links to all datasets and an option to search for a specific dataset that is part of the connection.
Selecting a dataset name opens the dataset in the Experience Platform UI in a new tab.
The status of importing new data for datasets:
The status for backfill data across datasets.
To configure which columns to display select
Edit a connection
- Select
- Select
Alternatively, you can:
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Select the connection row.
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Select
When editing a connection, you can:
- Start and stop importing new data.
- Rename a connection.
- Refresh the dataset/s.
- Remove dataset/s from the connections.
See Create or edit a connection for more information.
Delete a connection connections-delete
- Select
- Select
Alternatively, you can:
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Select the connection row.
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Select
When you delete a connection, a Delete connection panel indicates which data views are deleted and which workspace projects are affected.
Select Continue to delete the connection.
See Deletion implications for more information about deleting a connection.
Create a data view for a connection
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If no data view is associated with the connection:
- Select
- Select
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If one or more data views are already created for the connection:
- Select
- Select
- Select
Alternatively, you can:
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Select the connection row.
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Select
See Create or edit a data view for more information.
Connection details connection-detail
To go to the details for a connection, select a connection name in the connections table.
The Connections details interface provides a detailed view of the status of a connection. You can:
- Check the status of your connection’s datasets and of the ingestion process.
- Identify configuration problems that can cause skipped or deleted records.
- See when the data is available for reporting.
Summarize the event, lookup, profile and summary dataset records that are added, skipped, and deleted, and the number of batches added. These metrics are based on the dataset and date range you have selected.
Select Check detail to show the Check skipped detail popup. The popup lists the number of skipped records and the reason for all event datasets or selected dataset.
Select
Indicates how many rows were skipped in the selected time period, for the dataset and date range you have selected. Reasons for skipping records include: missing timestamps, missing or invalid person ID, and so forth. Updated every 10 minutes.
Invalid person IDs (such as undefined, or 00000000, or any combination of numbers and letters in a Person ID that appears in an event more than 1 million times in a given month) are IDs that cannot be attributed to any specific user or person. These rows cannot be ingested into the system and result in error-prone ingestion and reporting. To fix invalid person IDs, you have 3 options:
- Use Stitching to populate the undefined or all-zero user IDs with valid user IDs.
- Blank out the user ID, which are then kipped during ingestion (preferable to invalid or all-zero user IDs).
- Fix any invalid user IDs in your system before ingesting the data.
Indicates how many rows were deleted in the selected time period, for the dataset and date range you have selected. Someone might have deleted a dataset in Experience Platform, for example. Updated every 10 minutes.
In some scenarios, this value can also include records replaced, as with stitching or some lookup dataset updates. Consider this example:
- You upload one record to an XDM Individual Profile dataset, which Customer Journey Analytics is configured to ingest as profile lookup data. In the connection details, this dataset would display 1 record added.
- You upload a duplicate of the original record into the same AEP dataset, which now contains two records. Customer Journey Analytics ingests the additional record from the profile lookup dataset. Seeing that it has already ingested a profile record in the connection for that person ID, Customer Journey Analytics deletes its earlier version and adds the new profile data. In the connection details, this action would represent 1 record added and 1 record deleted, because Customer Journey Analytics only retains the most recent profile lookup data for any ingested person ID.
- In total, the AEP dataset contains two records that happen to be identical. Separately, the Customer Journey Analytics connection details display the status of its ingested data: 2 records added and 1 record deleted for this profile dataset.
The status of importing new data for the dataset:
The transformation status of applicable B2B lookup datasets. See Transform datasets for B2B lookups for more information.
N/A for all other datasets, not applicable for transformation.
The status of backfill data for the dataset.
The status of importing new data for the dataset:
The status of backfill data for the dataset.
Connection panel
When no dataset is selected in the datasets table, a panel on the right side of the Connections interface shows connection options and details.
The status of importing new data for datasets:
The status of backfill data for datasets.
The transformation status of applicable B2B lookup datasets. See Transform datasets for B2B lookups for more information.
Dataset panel
When a dataset is selected in the datasets table, a panel on the right side of the Connections interface show details for the selected dataset.
How many rows were skipped during ingestion in the selected time period.
Reasons for skipping records include: Missing timestamps, missing or invalid person ID, and so forth. Updated every 10 minutes.
Invalid person IDs (such as undefined, or 00000000, or any combination of numbers and letters in a Person ID that appears in an event more than 1 million times in a given month) are IDs that cannot be attributed to any specific user or person. These rows cannot be ingested into the system and result in error-prone ingestion and reporting. To fix invalid person IDs, you have 3 options:
- Use Stitching to populate the undefined or all-zero user IDs with valid user IDs.
- Blank out the user ID, which is then skipped during ingestion (preferable to invalid or all-zero user IDs).
- Fix any invalid user IDs in your system before ingesting the data.
The status of importing new data for the dataset:
The status of backfill data for the dataset.
To show a dialog with an overview of the past backfills for the dataset, select
Usage
The Usage interface shows the usage of ingested and reportable rows across all connections. This interface supports you to determine whether your Customer Journey Analytics usage complies with what is contractually agreed upon. In addition to monitoring purposes, you can use the Usage UI to plan your Customer Journey Analytics license renewal.
You can select a time range (between last 6 months, year to date, or last 2 Years) and an interval (between monthly or quarterly) to monitor Customer Journey Analytics usage. The interface is divided into two sections:
- Ingested rows: total rows ingested/sent from event datasets across all Customer Journey Analytics Connections, including records skipped during ingestion
- Reportable rows: total reportable rows that include all events data across all Customer Journey Analytics Connections
Select the Usage tab to access the interface.
Report on usage
- Select a Time range. You can select between Last 6 months, Year to date, or Last 2 Years.
- Select an Interval. You can select between Monthly or Quarterly.
For Ingested rows:
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A panel displays the total ingested rows that include all the events data across all the connections updated on every 2nd day of a month. Within the panel:
- a box displays the number of ingested rows for the last month and the change in % (indicated by â–² or â–¼) from the previous month.
- a line graph displays the ◼︎ Monthly ingested rows.
To see a popup that displays the number of monthly ingested rows for a month, hover over any data point in the line graph.
For Reportable rows:
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A panel displays total reportable rows that include all the events data across all the connections updated on every 2nd day of a month. Within the panel:
- a box displays the cumulative total number of reportable rows.
- a box displays the total number of reportable rows for the last month and the change in % (indicated by â–² or â–¼) from the previous month.
- a line graph displays the ◼︎ Monthly reportable rows.
To see a popup that displays the number of cumulative reportable rows for a specific month, hover over any data point in the line graph. - a line graph displays the ◼︎ Cumulative reportable rows.
To see a popup that displays the number of monthly reportable rows for a month, hover over any data point in the line graph.