There are multiple BI tools available for connecting different data sources and present the data visualization. Among them, Power BI is another powerful BI tool that allows analysts to build dynamic data models. Users can link to different data sources, collect or import data from these sources and create an effective data model. Also, users can build various power bi reports and visuals. Power BI data modeling is one of the most important features of Power BI development.
Data modeling refers to connecting multiple data sources using a relationship. A relationship in Power BI data modeling describes how various data sources are linked with each other. It also allows you to build attractive data visuals on multiple data sources. These visuals can help to draw attention to have clear business data.
We can import the data relevant for creating data models, reports, and visuals from the following data sources. Such as:
- File data sources like Excel, PDF, XML, Sharepoint, etc.
- Power platform data sources like Power BI, workflows, etc.
- From Databases like SQL, Oracle, Teradata, MySQL, Snowflake, Amazon Redshift, etc.
- Online Service data sources like Salesforce, GitHub, MS Exchange, Google Analytics, Adobe, etc.
- Other data sources like ODBC, Python, R, Web, Sharepoint, etc.
Hence, this is an intro to Power BI data modeling. It explains what is PBI data model is in brief. Let us elaborate on this in the further paragraphs of PBI data modeling.
Introduction to Power BI Data Modeling
The major purpose of power bi data modeling is different from the data models within transactional systems. We can define a data model as an arrangement and relationship of two or more sets of a data table. In order to set the relation between the two objects, in Power BI, the user needs to drag a simple line between those common columns. Also, the relationship is visible in the data model of this BI tool. It will clearly give a glimpse of the power reports.
There are many ways to present these data models but the effective and common way is the Entity-Relationship Diagram. This data model represents the data model reports using an ERD.
However, a well-designed Power BI data modeling system must be optimized for data queries. Also, it minimizes the data set size by combining the whole data within the model. Most of the users don’t have the luxury of having a pre-built data warehouse or model. Therefore, they end up building a powerful Power BI data model in some cases.
By using the Power BI data modeling feature, it is easy to build custom calculations on the currently available tables. Later, you can present these columns within PBI visualization directly. Moreover, this feature allows business entities to describe the latest metrics. And it allows those custom calculations to perform for them.
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What do we need data modeling?
The following terms will explain the need for having PBI data modeling.
- A user can build a good relationship between different data sources and can connect them easily. Through this relationship, users can link with multiple data sources to create various reports and visuals.
- Power BI data modeling allows us to create a relation between two or more tables easily.
- Also, we can manage the relationship between these tables using the Power BI Desktop home tab.
- You can easily optimize data by hiding fields and sorting out the data ready for visualization.
- To execute calculations on your data, you can develop a measure.
Types of data modeling in Power BI
There are different types of Power Bi data modeling available that can connect to on-premise or cloud-based data sources. They are:
- Import data
- Direct Query
- Composite mode
- Live Connection
The Import data mode in the Power BI data modeling defines importing data into Power BI cache memory. Therefore, this is the standard method for developing visuals and other BI models. After we import data and save the PBD solutions, it saves the import data into a disk. Now the data is successfully loaded into the PBI cache, you can easily query data or develop visuals. Moreover, this data model of PBI supports the delivery of speedy performance for data visuals. Also, the model supports different features like Quick Insights, etc.
But it needs to have sufficient memory in your device to import and refresh data and file size cannot exceed 1GB for the free version.
In the Direct Query model, the PBI doesn’t import data instead it retains the metadata the elaborates data model structure. Moreover, this PBI model is best suitable for the data that exists in relational databases.
The Composite data model is useful to integrate both the above models - import and direct query. Further, this model can help in delivering the best performance from both the data models.
The final one, Live Connection mode doesn’t offer to import data into the Power BI cache. And it doesn’t store a copy of metadata within it. This mode is best suitable for the case where the data model is already in use. Also, it supports SSAS datasets, PBI Data sets, along Azure Analysis Services.
Thus, we have gone through the different data model types in PBI. Now let us move to the Power BI data modeling examples to understand them more clearly.
Power BI data modeling importance
Creating a good data model for business analysis is quite a challenge but it is important also. Because the goal is different for the Power BI data modeling compared to the database that runs a transactional application. In order to record sales, there is a need for a good database that is optimized to gather and store data safely. Similarly, to analyze those sales, you will need a data model that helps to optimize queries and gather data in volumes.
Hence, Power BI data modeling offers the same experience that we get with Power Pivot and Analysis Services Tabular. Moreover, you can create a good data model using the data as it is from the data source. Then you design relationships and later you design the power reports. Further, this approach may raise several problems when there are too many tables exist representing the same entity through data sources. For this problem, the solution comes from the Power Query and M language which can better handle it.
If you get the right data, then you can build a strong Power BI ecosystem. There is plenty of data available today from different sources but you need the right data to come up with a well-designed data model. Moreover, this will guarantee you to present accurate figures supporting the business logic which will help to perform the report well.
Benefits of Power BI data modeling
In the Power BI data modeling, a good data model must perform the following things.
Perform quickly and well
Should follow the business-driven approach
Reduce the complexity level
Maintainable with less or low costs, etc.
Improve the productivity of working people
Thus, there are many more advantages of using Power BI data modeling in a real scenario. It will provide the creation of a data model or an approach that will smoothen the data connection from different sources.
How to create calculated columns in Power BI data modeling?
You can easily build calculated columns within the Power BI data modeling. This can be done by combining two or more elements/tables of the data. Also, you can calculate on the existing column to describe a new metric. Otherwise, you can join the two columns to build a single column.
Further, you can build this column to create a relationship between the two different tables. Also, this can be useful to set up a relationship between the two tables.
You can create this column using the Data View tab on the PBD menu and then click on the button Modeling. From here you can get the New Column option and by entering the DAX formula you can execute the calculation.
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Creating Calculated Tables using Power BI data model
You can use a similar process as above to create Calculated Tables in the Power Bi data modeling.
Moreover, you can also manage the time-based data where PBI by default allows you to drill through time-based data. We can enable the drill feature in the data visualizations where it drills down to the next level of time hierarchy/structure which we can see as Years-Months-Quarters.
In this way, we can create different types of tables using the data model.
Summary
Here, I conclude the Power BI data modeling and its importance in the business areas. I hope you got the basic idea of what is PBI data model is and its uses to create different tables, columns, etc. PBI is not an ordinary reporting tool but a data modeling tool also where we can connect multiple data sources. These data sources can be of different types and from different locations.
We can easily develop a single data model by combining various data sources easily in Power bi data modeling. It also helps to improve productivity by reducing complexity. Also, it is easy to maintain and useful for the quick performance of a task.
Thus, to know more about this space attend the demo at Power BI Online Training with the ITGuru platform. This learning may help you get more skills on the Power BI tool to make your career brighter. This skill can also enhance your career development chances. Will bring more updates on similar topics in future blogs, till then keep watching this space.