We Can use Power BI streaming datasets to easily build real-time dashboards by pushing data into the REST API endpoint, and having that data update in seconds on their streaming visuals
For Creating Streaming Datasets
First Of all Go To Power BI Web and click on Streaming Datasets option .
From there, we will create a dataset of type API
We Can Name the dataset as We want but necessary to remember Then, add the following fields to the streaming dataset
For Twitter We can Create data with time (DateTime) , tweet (Text) , sentiment (Number)
Now For Create a Flow which will push Tweets and their sentiments to Power BI. Go To https://flow.microsoft.com/en-us/
Sign in, and then go to “My Flows”, then “Create from blank.”
Now, go to Twitter category, and select the “When a new tweet is posted” and Use Power BI For Search term .It will start a Flow whenever a tweet which contains the search term is posted.
we will pipe these Tweets into the Microsoft Cognitive Services Sentiment Detection Flow action to understand the positivity of the Tweet content. This action takes in a Tweet, and outputs a number from negative to positive.
Select “New Step,” then “Add an action,” then search for “Sentiment Analysis” and select the “Cognitive Services Text Analytics – Detect Sentiment” action For That We Need API Key
entered API key and Information in it and pipe in the Tweet text to the sentiment detection action by selecting it from the dynamic content pane on the right side of the screen. Flow should now look like the following
Now Go to “New step,” then “Add an action,” and then enter “Power BI” into the search box. Select “Add row to streaming dataset” from the actions list
Select the name of the workspace, then the name of the streaming dataset in the first step, and select the Table titled “RealTimeData.” Note that all streaming datasets created in powerbi.com will have one table named “RealTimeData.”
Create a dashboard with a streaming visual in Power BI. Go To Power BI Web , go to a dashboard, select “Add tile,” then “Custom streaming data” and finally the name of the streaming dataset that we created in the first step.
Dashboard is Look Like