As soon as you start building your first solution in Power BI, it is especially important to understand which components are available in Power BI and for which activities they are used. Therefore, in this article I will introduce you to the essential activities for developing a reporting solution and show you which basic components in Power BI are used for this.
The core activities for building a reporting solution
Data does not analyze itself, but requires various activities so that information can be extracted from data. Below you will find the four essential activities, which are performed at some point in almost every reporting solution.
|Data creation||Data creation typically takes place within the systems that are used for the operational control of business processes. Therefore, these systems are also referred to as source systems in the BI context.|
|Data integration||As a rule, a key challenge is that data is distributed in different systems, tables and formats. In addition, the data is stored in the source systems in such a way that it is optimized for transactional processing, also called OLTP (Online Transactional Processing). However, we want to put the data into a form that keeps the data optimized for analytical purposes, also called OLAP (Online Analytical Processing). Data integration describes the process of selecting, harmonizing and providing data for analytical purposes. In this context, the abbreviation ETL a special role. ETL stands for Extract, Transform and Laden. It thus describes a process in which data is extracted from a source, then transformed using specified rules, and then loaded into a destination.|
|Data modeling||The mere presence of the integrated data is not yet sufficient to evaluate it effectively and scalably. In the context of data modeling, we link the data in the form of a so-called data model. A data model is a data-based representation of the use case you want to analyze. A good data model follows the star schema, which is why I have given the star schema a separate Post . Once the data model is available, additional calculations (metrics and KPIs) will be added so that they can be used in the final phase, data presentation.|
|Data presentation||Now we have arrived at the actual end product. As part of the data presentation, we visualize the data and calculations contained in the data model so that they can be consumed by the report recipients. Various media come into question for this, for example reports, dashboards, presentations or apps.|
The following picture shows the interaction of the various core activities.
The basic Power BI components
Now we come to the absolute basic components of a Power BI solution. For starters, I'm really focusing on the components that are relevant in almost every solution. Beyond that, let's classify the components into the core activities described above.
Power BI Desktop
Power BI Desktop is a business intelligence software from Microsoft. With Power BI Desktop you can:
- connect to various data sources
- transform the data
- then transfer the data into a data model
- calculate metrics and KPIs based on the data model
- Build reports based on the data model including the calculated metrics, KPIs and dimensions.
Data integration: Power Query for ETL activities
Data Modeling: Data View, Data Model View and Measures
After data has been loaded into Power BI Desktop via Power Query, it can be linked to a data model in the Data Model view. In addition, using so-called measures, calculations can be performed on the data model and the data it contains.
In addition, there is the Data view. In addition to various functions, you can view and filter the individual data sets of all loaded tables.
Data presentation: report view and visuals
In the last view, the Report view, you can drag and drop your report based on the data model, the data it contains and the measures. This is done with so-called visuals, into which the columns and measures can be inserted. There are a number of standard visuals, e.g. line chart, KPI map, scatter chart and more. However, so-called custom visuals can also be used. These are partly provided by Microsoft and partly by other providers.
Power BI Service
What is the added value of the Power BI Service?
Now the question quickly arises why the Power BI Service is actually needed when Power BI Desktop has a wide range of functions in the areas of data integration, data modeling and data presentation?
From my perspective, the most important reason to use the Power BI service is to share reports with report recipients. While you could also directly distribute the Power BI Desktop file (pbix file) to the report recipients, this leads to the following problems.
Authorization management and data security: When you give a Power BI Desktop file to someone, that person also has access to all the data stored in the Power BI Desktop file. This also applies if you set various filters in the report view in advance, since the file owner also gets access to the data model and all the data it contains. Solutions based on Direct Query are an exception. However, this is not the rule. In the Power BI service, however, you can control who is allowed to see what and what is not.
User friendliness: Since the focus of Power BI Desktop is on solution development, there is no real read mode here either. This means that the owner of the file can also change the solutions and thereby logics (accidentally). There is no password-based locking as in Excel. On the other hand, in Power BI Service shared reports are in read mode by default. Of course, the consumer can still use the report interactively by filtering and the like, as far as allowed.
Version distribution: If you control the distribution of reports via file distribution or file provisioning, you always run the risk that the recipients will later look at an outdated version. With the Power BI service, you always ensure that all recipients are looking at the same and, above all, the latest version.
For these reasons, I recommend working with the Power BI service right from the start. This can also take place on a small scale with just a few people at first.
Organize your content in the Power BI service
In our example, we load the solution into the workspace named "Finance".
After I upload a solution developed in Power BI Desktop to a workspace, it is split into two objects
- The Datasetwhich contains the ETL logics from Power Query, the data model, the data and the measures (lower image in orange).
- The Reportwhich contains the report pages and visualizations (lower image in blue)
If we click on the report from the workspace, we will get to the report page previously created in Power BI Desktop.
From the Power BI service, there are now various options for sharing the datasets and reports with others. However, we will look at this separately in another article.
Finally, the following image shows the relationship between the core activities presented, Power BI Desktop and Power BI Service.
This article gave an introduction to the essential activities for creating a reporting solution and introduced the main Power BI components based on the core activities. I hope you were able to take away the following points from the article.
- Building a reporting solution requires Data creation, Data integration, Data modeling and Data presentation
- Power BI Desktop primarily provides functionality for the latter three activities
- Data integration is used with Power Query performed
- The Data model is used in the Data Model View performed
- The Data presentation happened in the Report View
- Based on the data model and the data Calculations to perform, we use Measures
- For the Share of your solutions you should always refer to the Power BI Service set
- Solutions published in the Power BI Service are delivered via Workspaces organizes
- Within the Workspaces the Power BI Desktop solutions are automatically Datasets and a Reports disassembled
- The Dataset includes the ETL logic from Power Query, the Data model, the Data as well as the Measures
- The Report includes the Report pages and the Visualswhich represent the information from the dataset.
I'm so glad you made it this far! Stay datenhungrig and see you soon!