Automic Automation Cloud Integration: Azure Power BI Agent Integration

Instantly inherit the advanced capabilities of your enterprise solution, enabling you to deliver your digital transformation more quickly and successfully. This video explains the Automic Automation Azure Power BI agent integration and its benefits. It presents its components and demonstrates how to install, configure, and use it. 

Video Transcript

Welcome to this video on the Automic Automation Azure Power BI integration solution. In this video, we will explain the Azure Power BI cloud integration and what it brings to the Automic Automation user community.

Azure Power BI core functionality revolves around reporting and data analytics. It allows users to fetch data from various sources, process it, and display it to users. One of its strengths is the ability to refresh data sets. Integrating Automic Automation with Azure Power BI allows you to run Azure Power BI jobs in your workspace from Automic Automation.

We'll provide some technical insights so that the integration components are clearly identified and the deployment sequence is understood. We'll focus on the configuration of the agent and the design of the two core object templates: connections and jobs. Finally, we'll run through a demo.

Orchestration and Centralized Management

Automic Automation plays a central role in orchestrating operations across multiple environments, including the cloud. Automic Automation synchronizes these processes with other non-cloud operations. By integrating Azure Power BI, we can configure process automation centrally in Automic Automation and then trigger, monitor, and supervise everything in one place.

Azure Power BI processes can then be synchronized with all other environments routinely supported by Automic Automation. The Azure Power BI role is reduced to executing the jobs; all other functions, especially those pertaining to automation, are delegated to Automic Automation. This means that we don't have to log into the Azure Power BI environment and keep on refreshing it by ourselves. Automic Automation handles all the execution and monitoring aspects.

The Automic Automation integration provides a simplified view of Power BI jobs. Automic Automation lets us build configurations with intuitive interfaces like drag-and-drop workflows and supervised processes in simple dashboard tools designed natively for operations. Statuses are color-coded, and retrieving logs is done with a basic right-click. From an operations perspective, Automic Automation significantly simplifies the configuration and orchestration of Azure Power BI jobs.

Advanced Workflow Configurations

Externalizing operations to a tool with a high degree of third-party integration means we can synchronize all cloud with non-cloud workloads using various agents and job object types. We can build sophisticated configurations involving multiple applications, database packages, processes like backups and data consolidation, file transfers, web services, and other on-premise workloads.

A conventional architecture involves two systems: the Automic Automation host and a dedicated system for the agent. The agent is configured with a simple INI file containing standard values: system, agent name, connection, and TLS.

When we start the agent, it connects to the engine and it adds two new objects to the repository:

1. A connection object to store the Azure Power BI endpoint and login data.

2. A job template designed to trigger Azure Power BI jobs.

Deployment and Agent Setup

Let's assume we're automating for four instances of Azure Power BI. We create a connection object in Automic Automation for each instance by duplicating the connection template for each of these instances. Lastly, we create the Azure Power BI jobs in Automic Automation for each corresponding process in Azure Power BI.

The Automic Automation jobs include the connection object based on the target system. When we execute the jobs in Automic Automation, it triggers the corresponding process in Azure Power BI. We're able to retrieve the successive statuses and finally generate a job report in Automic Automation. This job can be incorporated into workflows and integrated with other non-cloud processes.

The procedure to deploy the Azure Power BI integration is as follows:

• First, we download the integration package from Marketplace.

• We unzip this package, which produces a directory containing the agent, the INI configuration files, and several other items like the start command.

• We use the appropriate INI file for our specific platform.

Azure Power BI is a standard Automic agent. It requires at least four values to be updated: agent name, Automic system, JCP connection and TLS port, and finally, the TLS certificate. When the agent is configured, we start it. New object templates are deployed. We create a connection to every Azure Power BI instance we need to support. For this, we use the template connection object, which we duplicate as many times as we need.

The connection object references the Azure Power BI endpoint. Finally, we use the Azure Power BI template jobs to create the jobs we need. We match these Automic Automation jobs to the Azure Power BI jobs, reference the connection object, and run them. We're able to supervise the jobs, generate logs, and retrieve the statuses. The jobs can then be incorporated into non-cloud workflows.

Technical File Installation

We install, configure, and start an agent to deploy the Azure Power BI integration. The agent is included in the Azure Power BI package which we download from Marketplace. We unzip the package which creates a file system: agent/Azure Power BI/bin that contains the agent files.

Based on the platform, we rename the agent configuration file UCXJCX and set a minimum of four values: the agent name, the AE system name, the host name and port connection to the automation engine's JCP, and finally, the directory containing the TLS certificate.

Finally, we start the agent by invoking the JAR file via the Java command. The agent connects to the AE and deploys the object templates needed to support the integration: the connection object and the Azure Power BI job templates.

Demo: Azure Power BI Console and Automic Setup

In our demo, we will create a connection object. Once we have established the connection to the Azure Power BI environment, we'll create a refresh data set job. Finally, we'll execute and supervise this job.

Let's explore the Azure Power BI console. This is where you monitor your jobs and interact with your work and data sets. Upon entering the console, you'll immediately notice workspaces. You can see your "My Workspace" here, and you also have the option to create a new workspace. These workspaces are essential as they house the data sets where your jobs will run.

Within a chosen workspace, such as the WLA workspace we've seen, you'll find your data sets. For instance, selecting a specific data set like "WLA data set" allows you to perform actions like refreshing it. Once you initiate a refresh, you can then access the refresh history. The refresh history is crucial for monitoring your jobs. Here you can see the status of all jobs—whether they are running, completed, or in progress. You'll find important details such as request IDs, start and end times, and other relevant information.

Connection Configuration and Authentication

Let's move on to the Automic system. Here we create a connection object with specific inputs to connect to Azure Power BI. This object is crucial as it establishes the link between Automic and your Power BI environment. The first field you'll see is "Base URL." This is where you need to provide the endpoint for your Power BI service.

Next, we come to the authentication types. We have three options:

1. The Service Principal type.

2. The OAuth 2 token type.

3. The Token from File type.

If you choose Service Principal, you will need to provide three specific credentials: the Tenant ID, the Client ID, and the Client Secret. For OAuth, you need to specify the version you are using (version one or version two). If you select version one, you will need to provide a resource; if you select version two, you will need to provide a scope. The OAuth 2 token authentication type requires you to specify a token.

The third option is Token from File. This is the chosen method for this demo, and it allows you to directly specify a file containing the authentication token. If you are using a proxy, you can specify the proxy host name, port, username, and password in the proxy section. And that's how you create and configure a Power BI connection object. This object will then be used to map to your Power BI jobs, allowing Automic to seamlessly interact with your environment.

Job Execution and Monitoring

Once the connection object is defined, you can create a refresh data sets job. The connection drop-down list lets us map a previously created connection object with this particular job. After selecting the connection, you select the workspace. You can search for the workspace by clicking a picker button which will list all workspaces related to the connection.

For the demo, we use the WLA workspace. Once the workspace is selected, you then select the specific data set from within that workspace. This is also done by clicking a picker button. Next, go to the attributes page of the job and make sure you select the agent that corresponds to the agent name you configured in the INI file. These configurations are essential before saving and executing the job.

Everything is configured now, so we can execute the job within the AWI portal. The system will then show the job as active in the executions view. Simultaneously, you can switch to the Power BI console by navigating to the relevant workspace and data set and then checking the refresh history. You can monitor the status of the job. Once the job is completed, its status will update in both the Automic AWI portal and the Power BI console.

In the executions view, you can see the job we have created has ended. You can then open the report within the AWI portal for that specific job. This report will display details including a request ID. You can compare this request ID from the Automic report with the request ID shown in the Power BI console's refresh history to confirm that the same job executed successfully.

Within the Automic AWI portal, you can also monitor the logs for the executed job. These logs provide information about the input fields and the running logs of the job. That wraps up the demo on how Automic Automation allows you to schedule, execute, and monitor Power BI reports and data sets directly from its interface, ensuring seamless coordination and reducing manual intervention. Thank you for watching this video.

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