Power Fm 987 Live Stream
Thora Buckner
I think that stream on sharepoint is doing live events but do you know if we could build a such solution on a power page website for exemple ? Is it even possible on a standard sharepoint ?
Kind regards
hey, friends I created a streaming dataset into a workspace but when I am trying to pull that dataset into the power bi desktop it is showing that (reply-dataset name either the database doesn't exist or you don't have permission to access it) I also tried from another person workspace it still saying the same or value cannot be null or parameter = data source? i also activated the direct query mode from the setting still the same.
Power BI with real-time streaming helps you stream data and update dashboards in real time. Any visual or dashboard created in Power BI can display and update real-time data and visuals. The devices and sources of streaming data can be factory sensors, social media sources, service usage metrics, or many other time-sensitive data collectors or transmitters.
Because there's an underlying database that stores the data as it arrives, you can create reports with the data. These reports and their visuals are just like any other report visuals. You can use all of Power BI's report building features, such as Power BI visuals, data alerts, and pinned dashboard tiles.
Once you create a report using the push semantic model, you can pin any of the report visuals to a dashboard. On that dashboard, visuals update in real time whenever the data is updated. Within the Power BI service, the dashboard triggers a tile refresh every time new data is received.
A streaming semantic model also pushes data into the Power BI service, with an important difference: Power BI stores the data only into a temporary cache, which quickly expires. The temporary cache is used only to display visuals that have some transient history, such as a line chart that has a time window of one hour.
A streaming semantic model has no underlying database, so you can't build report visuals by using the data that flows in from the stream. Therefore, you can't use report functionality such as filtering, Power BI visuals, and other report functions.
The only way to visualize a streaming semantic model is to add a tile and use the streaming semantic model as a custom streaming data source. The custom streaming tiles that are based on a streaming semantic model are optimized for quickly displaying real-time data. There's little latency between pushing the data into the Power BI service and updating the visual, because there's no need for the data to be entered into or read from a database.
In practice, it's best to use streaming semantic models and their accompanying streaming visuals in situations when it's critical to minimize the latency between pushing and visualizing data. You should have the data pushed in a format that can be visualized as-is, without any more aggregations. Examples of data that's ready as-is include temperatures and pre-calculated averages.
With a PubNub streaming semantic model, the Power BI web client uses the PubNub SDK to read an existing PubNub data stream. The Power BI service stores no data. Because the web client makes this call directly, if you allow only approved outbound traffic from your network, you must list traffic to PubNub as allowed. For instructions, see the support article about approving outbound traffic for PubNub.
As with the streaming semantic model, with the PubNub streaming semantic model there's no underlying Power BI database. You can't build report visuals against the data that flows in, and can't use report functionality like filtering or Power BI visuals. You can visualize a PubNub streaming semantic model only by adding a tile to the dashboard and configuring a PubNub data stream as the source.
Tiles based on a PubNub streaming semantic model are optimized for quickly displaying real-time data. Since Power BI is directly connected to the PubNub data stream, there's little latency between pushing the data into the Power BI service and updating the visual.
You can use Power BI REST APIs to create and send data to push semantic models and to streaming semantic models. When you create a semantic model by using Power BI REST APIs, the defaultMode flag specifies whether the semantic model is push or streaming.
If no defaultMode flag is set, the semantic model defaults to a push semantic model. If the defaultMode value is set to pushStreaming, the semantic model is both a push and streaming semantic model, and provides the benefits of both semantic model types.
When you use semantic models with the defaultMode flag set to pushStreaming, if a request exceeds the 15 KB size restriction for a streaming semantic model, but is less than the 16 MB size restriction for a push semantic model, the request succeeds and the data updates in the push semantic model. However, any streaming tiles temporarily fail.
When Historic data analysis is disabled, as it is by default, you create a streaming semantic model as described earlier. When Historic data analysis is enabled, the semantic model you create becomes both a streaming semantic model and a push semantic model. This setting is equivalent to using the Power BI REST APIs to create a semantic model with its defaultMode set to pushStreaming, as described earlier.
Streaming semantic models created by using the Power BI service UI don't require Microsoft Entra authentication. In such semantic models, the semantic model owner receives a URL with a rowkey, which authorizes the requestor to push data into the semantic model without using a Microsoft Entra ID OAuth bearer token. However, the Microsoft Entra ID approach still works to push data into the semantic model.
You can add Power BI as an output within Azure Stream Analytics, and then visualize those data streams in the Power BI service in real time. This section describes the technical details of that process.
Azure Stream Analytics uses the Power BI REST APIs to create its output data stream to Power BI, with defaultMode set to pushStreaming. The resulting semantic model can use both push and streaming. When you create the semantic model, Azure Stream Analytics sets the retentionPolicy flag to basicFIFO. With that setting, the database that supports the push semantic model stores 200,000 rows, and drops rows in a first-in first-out (FIFO) fashion.
If your Azure Stream Analytics query results in very rapid output to Power BI, for example once or twice per second, Azure Stream Analytics begins batching the outputs into a single request. This batching might cause the request size to exceed the streaming tile limit, and streaming tiles might fail to render. In this case, the best practice is to slow the rate of data output to Power BI. For example, instead of a maximum value every second, request a maximum value over 10 seconds.
On the Add a custom streaming data tile page, you can select an existing semantic model, or select Manage semantic models to import your streaming semantic model if you already created one. If you don't have streaming data set up yet, select Add streaming semantic model to get started.
This section describes the Power BI REST API and PubNub options, and explains how to create a streaming tile or semantic model from the streaming data source. You can then use the semantic model to build reports. For more information about the Azure Stream option, see Power BI output from Azure Stream Analytics.
The Power BI REST API makes real-time streaming easier for developers. After you select API on the New streaming semantic model screen and select Next, you can provide entries that enable Power BI to connect to and use your endpoint. For more information about the API, see Use the Power BI REST APIs.
After you successfully create your data stream, you get a REST API URL endpoint. Your application can call the endpoint by using POST requests to push your streaming data to the Power BI semantic model. In your POST requests, ensure that the request body matches the sample JSON that the Power BI user interface provided. For example, wrap your JSON objects in an array.
For streaming semantic models you create in the Power BI service UI, the semantic model owner gets a URL that includes a resource key. This key authorizes the requestor to push data into the semantic model without using a Microsoft Entra ID OAuth bearer token. Keep in mind the implications of having a secret key in the URL when you work with this type of semantic model and method.
The integration of PubNub streaming with Power BI helps you create and use your low-latency PubNub data streams in Power BI. When you select PubNub on the New streaming semantic model screen and select Next, you see the following screen:
You can secure PubNub channels by using a PubNub Access Manager (PAM) authentication key. This key is shared with all users who have access to the dashboard. For more information about PubNub access control, see Manage Access.
PubNub data streams are often high volume, and aren't always suitable for storage and historical analysis in their original form. To use Power BI for historical analysis of PubNub data, you must aggregate the raw PubNub stream and send it to Power BI, for example by using Azure Stream Analytics.
Here's an example of how real-time streaming in Power BI works. This sample uses a publicly available stream from PubNub. Follow along with the example to see the value of real-time streaming for yourself.
Streaming semantic models don't support filtering. For push semantic models, you can create a report, filter the report, and then pin the filtered visuals to a dashboard. However, there's no way to change the filter on the visual once it's on the dashboard.
You can pin the live report tile to the dashboard separately, and then you can change the filters. However, live report tiles won't update in real time as data is pushed in. You have to manually update the visual by selecting the Refresh icon at top right on the dashboard page.