Recently SQLBI introduced a new tool that helps you create Power BI Model with a simplified interface – Power BI Bravo!
The aim of this post is to share my experience with it and to compare it with other Power BI tools that are on the market for a long time.
Initial setup
Bravo is an open-source tool that you can download from https://bravo.bi/ or straight from https://github.com/sql-bi/Bravo/releases/tag/v0.9.1
After installing the open-source tool when you open a Power BI Desktop, you will see that it is installed under the External Tools sections. Alternatively, Bravo can access datasets published in the Power BI Service or .vpax files produced by VertiPaq Analyzer.
Here is what the home page looks like:
Available Menus
It contains four main menus:
- Analyse Model
- Format DAX
- Manage Dates
- Export Data
Starting with Analyse Model, I will go through all features that this tool offers for Power BI enthusiasts. The main use of the Analyse Model functionality is to understand what makes your existing model inefficient, and what slows down the overall reports performance.
On the main screen, you will see a list of all fields in your Power BI model. They are in descending order based on their cardinality which correlates to the size they take in the model. You can also browse for columns or select them from the treemap visual on the right.
It contains four main menus:
- Analyse Model
- Format DAX
- Manage Dates
- Export Data
1. Analyse Model Menu
Starting with Analyse Model, I will go through all features that this tool offers for Power BI enthusiasts. The main use of the Analyse Model functionality is to understand what makes your existing model inefficient, and what slows down the overall reports performance.
On the main screen, you will see a list of all fields in your Power BI model. They are in descending order based on their cardinality which correlates to the size they take in the model. You can also browse for columns or select them from the treemap visual on the right.
So what are these high cardinality columns? Well, these are columns that have a high percentage of unique values. The reason why we are looking for such columns is that the compression works much less efficiently when compressing high cardinality columns. The more unique values a column has, the less efficient the compression will be.
TIP 1: Pay attention to the highlighted columns in yellow. Bravo highlights for you what columns from the model that you are analysing are not being used in measures or as a reference between tables. These are potential fields that you can remove from your model, reduce its size and therefore improve it.
TIP 2: Check all of your report pages and make sure these columns are not used in some visual on your report before deleting them.
2. Format DAX
Format DAX is the next feature available in Bravo.
Under the hood, it is integrated DAX Formatter
