Power BI

We will begin our journey by pulling some sales data into Power BI from a text file, as well as making some minor manipulations to the data.

This lesson covers an introduction to create different visuals and cross filtering in Power BI.

This module covers how to publish your data into the Power BI service, explores the feature parity between desktop and cloud, and covers how to update your data in the service.

In this module we will look at the methods of sharing your BI artifacts. We explore sharing without using the cloud, as well as sharing both within and external to your “tenant”.

In this module we will explore collecting Budget data to link into our dashboards.  The only issue is that it comes from a completley different source: an Excel file.

This lesson focuses on the characteristics that are required in order to link tables together properly, as well as a key tip that will prevent getting yourself into trouble later.

One potential source of m:m (many to many) join issues in Power BI can be solved by combining multiple columns into a “composite key” in order to relate tables together.  This module will dig into this issue, as well as how we solve the problem.

In this module we’ll explore how to build dimensional tables that solve the m:m (many to many) linking problem, allowing you to create much more robust models.

This module shows how to apply de-aggregation to our sample model.  The end goal here is to avoid m:m (many to many) joins by creating new dimensional tables that can be linked into the model via 1:m (one to many) relationships.

Every model that will perform any kind of date/calendar intelligence must have a proper calendar table.  This lesson gives you the tips that you can apply to any model to build a calendar table that dynamically updates with your data.

It’s now time to build a dynamic calendar on the fly for our sample model. After identifying your calendar’s start and end dates, it’s one line of code, 5 clicks, 4 characters and the Enter key, and you’re set.

In this module we’ll explore how to use Power BI’s formula language to create re-usable “Measures”

It’s now time to apply our knowledge of basic formulas, and build measures that add business intelligence value to our sample model.

Creating measures is all very well, but you need to understand why they return what they do. In this module we will explain how to identify the filter context applicable to your data point, and how that influences the way measures are calculated.

This lesson explores the syntax and practical application of adding or subtracting measures from each other, and lays the groundwork for creating more complicated mathematical combinations

CALCULATE() is the super-charged SUM(anything) function, and mastering it is the secret to truly mastering Power BI’s formula language. In this module we will show how it works, as well as how it impacts basic measure calculation.

In this module we will review several CALCULATE() functions that live in a real model, showing how they work and add value to our business intelligence.

How do you ensure that your “All Time Sales $” retains it’s “All time” status when someone click on a visual and cross filters your report? This lesson is ALL() about the function that lets you do exactly that.

In this module we look at practical examples of using the ALL() function to modify and override the filter context, resulting in measures that only change when we want them to.

Quick measures are ready-rolled formula patterns built through a drag and drop user interface, allowing you to write really complicated formulas for you without having to learn all the nuances of Power BI’s DAX formula language.

In this module we will take a look at creating bar and column charts to display our data.

Line charts are a great way to show trends over time, and we’ll explore doing so here.

Combination charts combine line and column charts into a single visual.  They are very useful for showing values versus a standard or target.

Working with maps can be a bit tricky. This module will explain the good, the bad and the ugly of maps, as well as provide recommendations to avoid the ugly.

In this lesson we’ll explore Power BI pie charts, donut charts and their characteristics.

Also known as “breakdown trees”, this visual can show interesting statistics about the makeup of your data sets.

Cards are super useful in order to drive alerts. They also have a close cousin called the multi-row card. In this module we will explore both, as well as their pros and cons.

The matrix is similar (but not the same as) Excel’s PivotTable. In this lesson we will show how to work with this specific visual.

KPIs are great for showing you most recent period’s performance versus a target value, with a longer term trend as an area chart in the background.

When the default visuals are not inspiring, or you just need to use a visual that Microsoft doesn’t supply by default, you can often find another version in the Custom Visual store.  In this module we will look at the Chiclet Slicer, a vast improvement over Power BI’s native Slicer visual.