In this demo, I will show you how Universal Automation Center orchestrates every type of file transfer and seamlessly connects them with the applications that consume the data. With a single platform, you can orchestrate B2B file transfers, server-to-server transfers, inter-cloud data streaming, mainframe transfers, and Kubernetes integrations as part of the end-to-end workflow. The following demo workflow consists of three scenarios. The first scenario is a B2B data pipeline. The business partner publishes an encrypted daily price update file using the UDMG web transfer client. As soon as a file is uploaded, a universal event is triggered. The moment the universal event is detected, the monitor triggers a workflow in real time. The workflow automatically decrypts the file and transfers it into the SAP application server. Next, an SAP Integration Suite iFlow application validates the file and registers its metadata in SAP HANA Cloud Control table. Universal Automation Center then starts an SAP Data Services on BTP job to validate the business data and load it into a single staging table. Finally, a processing result file is generated, encrypted, and securely sent back to the business partner. Let's run this scenario. As soon as a partner drops a file onto their server, UAC detects it automatically. As you can see, the monitor picks up the file immediately and triggers a workflow. The file is decrypted, transferred to the SAP application server, validated, loaded, and processed in SAP. Then, the encrypted response file is returned to the partner. Finally, the Kong AI task collects a log output from each step in the workflow, compiles a structured execution report, and posts it automatically to the workload automation Slack channel, so your team has full visibility of every run without leaving Slack. Here you see the Slack channel with a complete history of the process flow. The second scenario starts when new sales data is uploaded to Microsoft SharePoint. As soon as the file arrives, the data is streamed directly from SharePoint to Microsoft OneLake. Once the data becomes available in OneLake, a Microsoft Fabric pipeline running a Power BI Gen 2 pipeline is started automatically. The workflow then waits until all required processing has been completed before triggering the Power BI refresh. At the same time, a second branch of the workflow prepares the data for AI processing. Before the data is used to train an AI model, sensitive information is anonymized using our new LLM task. The anonymization rules are simply defined in a markdown file. No programming is required. After the anonymization is complete, the workflow pauses for manual approval. I will approve the request using the Universal Portal, which then starts the Google AI agent Pipeline to train the model using the newly received sales data. Let's upload the new sales file to SharePoint and start the Microsoft fabric pipeline. At the same time, the AI anonymization task is executed. The output shows that sensitive data has been successfully masked. I will now approve the next step using the Universal Portal. The Universal Portal is a web UI that supports any device, mobile, tablet, or PC because it has a responsive design and is based on the React framework. It supports multiple languages of many of the European languages, for example. The approval immediately starts the Google AI Agent Pipeline. Let me go to the search scenario. This scenario is also triggered by the SharePoint upload. In this flow, data is transferred from the IBM mainframe into Kubernetes pod running in SAP Kyma. For demonstration purposes, I've placed the first task on hold so I can explain what's happening. The workflow first checks whether there's enough space available on the Linux server where the file is transferred to. If additional storage is required, Universal Automation Center automatically starts a Terraform workflow that provisions and attaches a new AWS EBS volume. Once sufficient storage is available, the file is transferred from the mainframe to the Linux server. A virus scan is then performed. If the scan succeeds, the data is transferred into the Kubernetes pod running in SAP Kyma, where a Kubernetes job processes the data. Once both the Kubernetes jobs and the Microsoft Fabric pipeline have completed successfully, the workflow automatically triggers the Power BI refresh, ensuring that the dashboard is updated only after all required data is available. Let me give you some additional notes. It's worth mentioning that you can also transfer data directly from the mainframe to a Kubernetes pod. This is possible because Universal Data Mover fully supports TLS with server name identification called SNI, enabling secure direct communication with Kubernetes ingress endpoints. Let me summarize. In this demonstration, you have seen how Universal Automation Center can integrate B2B file transfers with business partners into enterprise data pipelines, stream data directly from cloud applications such as Microsoft SharePoint to Microsoft OneLake, include AI tasks as part of a workflow from automated data anonymization to AI-based analysis of workflow executions with automatic report generation and Slack notification. Transfer data securely from IBM mainframe to Kubernetes, orchestrate third-party technologies such as Microsoft Fabric pipelines, Google AI Agent Pipelines, Terraform workflows, SAP Data Service, and SAP BTP applications within a single end-to-end automation workflow.
