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Transcript

Hi, this is Michelle. In this video, I鈥檒l show you how to export an experience platform data set to a cloud storage destination. Before we get into the demo, let鈥檚 review the benefits and use cases for this feature. Cloud storage destinations let you export raw, unstructured data sets using a guided workflow. The core tenets of data governance apply. Usage policies are enforced, as is the case for other types of destination workflows. Because of the interoperability between experience platform and public clouds, use cases involving data sets used in external systems is supported. These are some primary use cases for exporting data sets. Use an external machine learning and business intelligence tools to support analytical use cases, such as reporting and informing better audience creation. Monitor the health and performance of email marketing campaigns. And store data in external systems for compliance needs. These are the cloud storage destinations that can export data sets. If you don鈥檛 see the data set option in the export workflow, check your user permissions. You need view destinations and manage and activate data set destinations permissions. Also, ensure you have the proper data management permissions for data sets, specifically view data sets. Okay, it鈥檚 time for a demo, starting with the data set export connection. I鈥檓 logged into experience platform. Under connections, I鈥檒l choose destinations. In the destinations catalog view, I鈥檒l choose cloud storage under the categories heading. We鈥檒l see the workflow using an Amazon S3 account. While you may have existing Amazon S3 connections, a new connection is needed, specifically for exporting data sets. In the Amazon S3 destination card, click the data set icon at the top. This opens a new panel on the right. Click the configure new destination link. I鈥檓 using an existing account, since I have configured accounts already. If you don鈥檛, use the new account workflow. In the modal, choose an account from the list and then click on select in the right corner. Now it鈥檚 time to choose the data type. Three different types are supported for S3, but data sets is what I want. Next, fill in the name, description, bucket name, and folder path fields. The last two fields specify which area and path of the S3 account the data set files will be stored. There鈥檚 two options for file type, JSON and Parquet. I鈥檒l choose JSON. There鈥檚 an option for compression format, and that鈥檚 relative to the file type selected. Review and select the alerts you鈥檇 like to receive for this export. I鈥檓 finished with the configuration details in this step, so I鈥檒l select next up here. Now I need to select the appropriate marketing action. Data export is what I need. I鈥檒l select that and create the connection. Now I鈥檓 ready to send data to the new S3 connection. I鈥檒l choose the activate button for Amazon S3. Data sets is the data type. This shows me the connection I just set up. I鈥檒l choose this and go to the next step. Now I can browse and choose the data set I want to export. There are some Journey Optimizer data sets here, but I want this weekend data set. I鈥檒l go to the next step. There are some folder name and scheduling settings available. First, I can modify the folder path using any of the available macros to customize the name. Now we鈥檒l look at schedule settings. You can choose the export cadence of exports. Determine if you want full or incremental files. The first file includes all existing data in the data set, functioning as a backfill. There are frequency setting presets. Choose the one that works for your needs. Last, you can choose the date range for your data set export. I鈥檒l save these settings and advance to the review step. Once everything looks good, choose finish to kick off the export process. That鈥檚 it! It鈥檚 very straightforward to export data sets. After this, you would connect to the S3 bucket to confirm you see the files there. Thank you for watching this video.