What Are Power BI Semantic Models?

Business intelligence can be used to gather, store, and analyze data from corporate activities. In nearly real-time, BI provides comprehensive business analytics to support better decision-making. Power BI is a business analytics tool that enables you to share insights and visualize your data within your company, as well as incorporate them into your website or application. The solution’s value and adaptability, consisting of several goods and services, come from utilizing each component separately and maximizing their synergy.

This blog discusses the Power BI Semantic model, its key components, types of semantic models, key considerations, a step-by-step guide to making semantic models, and best practices for optimizing your semantic model.

What Is a Power BI Semantic Model?

In Power BI, a semantic model can be considered a logical layer that incorporates the calculations, transformations, and connections between data sources required to produce dashboards and reports. A semantic model is the only reliable source of information for reports throughout an organization.

Before creating reports and dashboards, consider a semantic model the final step in the data pipeline. Once a semantic model is shared with other people in the organization, they can use it to create as many reports and dashboards as they like.

Semantic models conceal the intricate technical elements behind reports, allowing both technical and non-technical users to focus on evaluating the data and responding to business inquiries. Semantic models are notable for their reusability and sharing capabilities.

Key Components of a Power BI Semantic Model

Several components make up semantic models:

• Data links to one or more data sources, which can be included in a composite model, imported, or accessed using DirectQuery.
• Changes that make the data clean and ready for reporting.
• Business rule-based metrics and computations were established to guarantee consistent reports derived from the semantic model. By doing this, inconsistencies between analysis and reports are prevented, and clarity is guaranteed.
• Users may concentrate on creating reports thanks to clearly defined links between tables without being aware of the underlying database structures and data models.

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Semantic Model Types 

Since each mode has advantages and disadvantages that you should be aware of, selecting the appropriate one when connecting to your data in Power BI is a crucial first step in developing a semantic model.

Power BI offers three different semantic model modes:

1. Import mode
2. DirectQuery mode
3. Composite mode

Here is a brief description of each mode:

1. Import mode

The data is fully loaded into the Power BI (.pbix) file. The data is compressed, optimized, and stored on disk by the Vertipaq storage engine whenever the Power BI report is refreshed. For report creators, this results in quick performance and a variety of design options. Additionally, import mode enables semantic model developers to use DAX functions to generate computations and measures, as well as the entire set of Power Query M language functions to prepare and modify data.

2. DirectQuery mode

Instead of storing the actual data, this mode saves metadata about the model structure. The data is obtained from the underlying data source when the model is requested (for example, by displaying a visual). This is very helpful when dealing with big data sets or when a report needs to include almost real-time data for commercial purposes.

3. Composite mode

DirectQuery and import modes are combined to create composite mode. When real-time data viewing and the power and performance of import mode are required, this mode can be helpful. By setting the table to dual storage mode, the Power BI service can select the most effective option based on the kind of query.

Building Your Power BI Semantic Model 

Key Considerations

Here are the key considerations regarding the Power BI semantic model:

• Models hosted by SQL Server analysis services require a gateway to perform real-time connection queries.
• Models hosted by Power BI that import data must be fully loaded into memory before you can query them.
• When source data isn’t immediately available online, Power BI-hosted models that employ import mode must use gateways and require refreshes to keep data up to date.
• Users can initiate an on-demand refresh in the Power BI service or schedule a refresh for Power BI-hosted import models.
• DirectQuery mode in Power BI-hosted models necessitates connectivity to the source data. To obtain up-to-date data, Power BI queries the provided sources of data. This mode must use gateways when source data cannot be accessed directly via the Internet.

A Step-by-Step Process for Building a Power BI Semantic Model

The steps you may already be taking while creating reports with Power BI Desktop are also involved in creating a semantic model. These five steps are mostly taken while developing the semantic model: 

• Use import mode, DirectQuery, or composite models to import or establish a connection to the necessary data sources.
• Make the data usable for users by cleaning and transforming it. This entails clearing out text-based data columns, fixing missing data, and eliminating duplicates, among other things. The particular needs of your data determine the specific transformation processes.
• Use sound data modeling techniques like the star schema to define the relationships between your data tables. You may learn the fundamentals of Power BI data modeling in our course.
• Make estimates and measurements according to your particular company’s needs.
• Publish your semantic model to the Power BI service after you are satisfied.

Best Practices for Optimizing Your Semantic Model 

The established best practice in an organization is to create as few semantic models as possible and use them as the source for Power BI reports.

Furthermore, it is also suggested that semantic models should be:

• Comprehensive so that all the components needed to address pertinent business problems should be included in semantic models. Additionally, these components should rationally and accurately represent company systems and procedures.
• Optimized to ensure that their visual loads, DAX computations, and refreshes are quick, even as the volume of data increases.
• Secure so that they provide the necessary security components (such as row-level security) to stop unwanted access to the data.
• Flexible so that it can adjust to shifting business needs.

Conclusion

Power BI has changed how we think about business intelligence by renaming datasets to semantic models. We may see data as a part of a broader semantic layer that is easily shared and reused, rather than as static elements of dashboards and reports. The three types of semantic models have advantages and disadvantages that should be considered before selecting the appropriate one when connecting to your data in Power BI.

Following the steps mentioned in this blog, you can create a semantic model and use best practices to optimize it. To utilize Power BI to its fullest for data analytics, you need reliable and consistent data. Sign up for Hevo’s 14-day free trial and seamlessly migrate your data for real-time analytics.

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