DAX Café – Calculation groups
In this DAX-related post I will enhance the WTA Grand Slam data model with calculation groups. But what exactly are calculation groups in Power BI? Occasionally, you might need to add many similar measures to your model. Oftentimes, this is the case with time intelligence measures. For example, you define measures in your model to calculate…
Let’s create a WTA Grand Slam data model for Power BI!
For this post I want to create a tabular data model in Power BI containing WTA Grand Slam tennis data. To give you a flavor of what we want to achieve, the visual diagram below shows the data model. But to get the data in this format, we have to do some work. A typical…
8. Tournament Overview report
We want our reports to look nice but also be functional, clean and uncluttered. Most people read from top to bottom and from left to right, and will scan with the eyes from the top left of the report page in a zig-zag motion across the screen. Having this in mind, we should think about…
7. Calculated columns and measures (DAX)
Let’s talk DAX! Data Analysis Expressions is the native formula and query language for Power BI Desktop, Power Pivot and SSAS Tabular models. 1. Calculated columns and Row context We use DAX to create calculated columns. Whenever we create a calculated column a row context is also created, that allows the calculated column to perform…
6. Modelling data
The Model view shows all the tables, columns and relationships in the model. A data model brings facts and dimensions together. Relationships are connections between tables using columns from each, and can be: 1 to Many, 1 to 1 or Many to Many. A star schema consists of one or more fact tables surrounded by…
5. Shape and transform data with Power Query (M) – part 2
The matches data contain several information in a flat format, We will split this data in several entities. Follow the steps in the previous post to remove the first 3 rows of the Matches query and expand the binary column on the remaining row. Rename the query to Matches Original. We will keep a copy…
4. Shape and transform data with Power Query (M) – part 1
In the previous post we have imported the wta_tennis data in Power BI. In the next couple of posts we will use Power Query to shape the data into the format and structure we need, Common data shaping tasks are: rename/remove tables and columns, remove rows, changing data types, merge/append queries, pivoting/unpivoting, splitting column by…
3. Accessing ADLS Gen2 files from Power BI
It’s time to grab our puzzle pieces and start putting together the edges. 🧩🧩Recall that we have loaded the wta_tennis data in the cleansed area of our data lake. We now want to connect to the data lake and load the files in Power BI desktop. From the Home tab in Power BI desktop, choose…
2. Phases in building a Power BI report
Power BI is a suite of business analytics tools to deliver interactive visualizations, business intelligence capabilities and insights from hundreds of data sources. Power BI Desktop is a free authoring tool that lets you connect to, transform and visualize your data. It is updated monthly with new features. Download and install Power BI Desktop from…
1. Sketching the Power BI reports
In the previous tutorials we have fetched the tennis_wta data, transformed and loaded it in the data lake. We will now use Power BI Desktop to connect to this data lake, load and further transform the data in Power Query, build a data model and interactive reports on top of it. But before we begin,…
3. Notebooks with Python – part 2
In this post, we will create a third notebook to prep and transform the wta_matches csv’s. Matches Notebook Launch the Databricks portal and create a new cluster, as shown in the previous post. Name the cluster matchesNotebook. Recall from the previous posts on ADF, that we have ingested the wta_matches files (53 in total) in…
2. Notebooks with Python – part 1
Now that we have provisioned a Databricks workspace and have created a Spark cluster, it is time to get spinning by writing our first notebook. A notebook is a collection of cells. These cells are run to execute code, to render formatted text, or to display graphical visualizations. A Databricks notebook cell can execute Python,…
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