Datasets in the Power BI service
This article provides a technical explanation of Power BI datasets.
Power BI datasets represent a source of data ready for reporting and visualization. There are five different dataset types, created in the following ways:
- Connecting to an existing data model that isn't hosted in a Power BI capacity
- Uploading a Power BI Desktop file that contains a model
- Uploading an Excel workbook (containing one or more Excel tables and/or a workbook data model), or uploading a CSV (comma-separated values) file
- Using the Power BI service to create a push dataset
- Using the Power BI service to create a streaming or hybrid streaming dataset
Except for streaming datasets, the dataset represents a data model, which leverages the mature modeling technologies of Analysis Services.
In our documentation, sometimes the terms datasets and models are used interchangeably. Generally, from a Power BI service perspective it's referred to as a dataset, and from a development perspective it's referred to as a model. In the context of our documentation they mean much the same thing.
There are two types of external-hosted models: SQL Server Analysis Services and Azure Analysis Services.
Connecting to a SQL Server Analysis Services model involves installing the on-premises data gateway, whether it's on-premises or VM-hosted infrastructure-as-a-service (IaaS). Azure Analysis Services doesn't require a gateway.
Connecting to Analysis Services often makes sense when there are existing model investments, typically forming part of an enterprise data warehouse (EDW). Power BI can make a live connection to Analysis Services, enforcing data permissions by using the identity of the Power BI report user. For SQL Server Analysis Services, both multidimensional models (cubes) and tabular models are supported. As shown in the following image, a live connection dataset passes queries to external-hosted models.
Power BI Desktop-developed models
Power BI Desktop - a client application intended for Power BI development - can be used to develop a model. The model is effectively an Analysis Services tabular model. Models can be developed by importing data from dataflows, which can then be integrated with external data sources. While the specifics on how modeling can be achieved is outside the scope of this article, it's important to understand that there are three different types, or modes, of models that can be developed by using Power BI Desktop. These modes determine whether data is imported into the model, or whether it remains in the data source. The three modes are: Import, DirectQuery, and Composite. For more information about each mode, see the Dataset modes in the Power BI service article.
External-hosted models and Power BI desktop models can enforce row-Level security (RLS) to limit the data that is retrieved for a certain user. For example, users assigned to the Salespeople security group can only view report data for the sales region(s) to which they're assigned. RLS roles are dynamic or static. Dynamic roles filter by the report user, while static roles apply the same filters for all users assigned to the role. For more information, see Row-level security (RLS) with Power BI.
Excel workbook models
Creating datasets based on Excel workbooks or CSV files results in the automatic creation of a model. Excel tables and CSV data are imported to create model tables, while an Excel workbook data model is transposed to create a Power BI model. In all cases, file data is imported into a model.
Distinctions, then, can be made about Power BI datasets that represent models:
- They're either hosted in the Power BI service, or are externally hosted by Analysis Services.
- They can store imported data, or they can issue pass-through query requests to underlying data sources, or they can use a mix of both.
Here is a summary of important facts about Power BI datasets that represent models:
- SQL Server Analysis Services hosted models require a gateway to perform live connection queries.
- Power BI-hosted models that import data:
- Must be fully loaded into memory so that they can be queried.
- Require refresh to keep data current, and must involve gateways when source data isn't accessible directly over the internet.
- Power BI-hosted models that use DirectQuery storage mode require connectivity to the source data. When the model is queried, Power BI issues queries to the source data to retrieve current data. This mode must involve gateways when source data isn't accessible directly over the internet.
- Models may enforce RLS rules, enforcing filters to limit data access to certain users.
To successfully deploy and manage Power BI, it's important to understand where models are hosted, their storage mode, any dependencies on gateways, size of imported data, and refresh type and frequency. These configurations can all have a significant impact on Power BI capacity resources. In addition, the model design itself, including its data preparation queries, relationships, and calculations, all add to the mix of considerations.
It's also important to understand that Power BI-hosted Import models can refresh according to a schedule, or be triggered on-demand by a user in the Power BI service.
In scenarios where the dataset owner might have left the organization and one needs to take over the ownership, use the "Datasets - Take Over In Group" API.
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