I think you misundstood my question.
Refer the link I have proved in my Post:
This text is from that link only and here it says clearly that Push Dataset create a New Database (undelined below) but I did not say which Database.
Types of real-time datasets
There are three types of real-time datasets which are designed for display on real-time dashboards:
- Push dataset
- Streaming dataset
- PubNub streaming dataset
First let's understand how these datasets differ from one another (this section), then we discuss how to push data into those each of these datasets.
Push dataset
With a push dataset, data is pushed into the Power BI service. When the dataset is created, the Power BI service automatically creates a new database in the service to store the data. Since there is an underlying database that continues to store the data as it comes in, reports can be created with the data. These reports and their visuals are just like any other report visuals, which means you can use all of Power BI’s report building features to create visuals, including custom visuals, data alerts, pinned dashboard tiles, and more.
Once a report is creating using the push dataset, any of its visuals can be pinned to a dashboard. On that dashboard, visuals update in real-time whenever the data is updated. Within the service, the dashboard is triggering a tile refresh every time new data is received.
There are two considerations to note about pinned tiles from a push dataset:
- Pinning an entire report using the pin live page option will not result in the data automatically being updated.
- Once a visual is pinned to a dashboard, you can use Q&A to ask questions of the push dataset in natural language. Once you make a Q&A query, you can pin the resulting visual back to the dashboard, and that dashboard will also update in real-time.
Streaming dataset
With a streaming dataset, data is also pushed into the Power BI service, with an important difference: Power BI only stores the data into a temporary cache, which quickly expires. The temporary cache is only used to display visuals which have some transient sense of history, such as a line chart that has a time window of one hour.
With a streaming dataset, there is no underlying database, so you cannot build report visuals using the data that flows in from the stream. As such, you cannot make use of report functionality such as filtering, custom visuals, and other report functions.
The only way to visualize a streaming dataset is to add a tile and use the streaming dataset as a custom streaming data data source. The custom streaming tiles that are based on a streaming dataset are optimized for quickly displaying real-time data. There is very little latency between when the data is pushed into the Power BI service and when the visual is updated, since there’s no need for the data to be entered into or read from a database.
In practice, streaming datasets and their accompanying streaming visuals are best used in situations when it is critical to minimize the latency between when data is pushed and when it is visualized. In addition, it's best practice to have the data pushed in a format that can be visualized as-is, without any additional aggregations. Examples of data that's ready as-is include temperatures, and pre-calculated averages.
