In this paper we focus on the development of ontology-based mechanisms allowing for creating taxonomies of business logic concepts unifying system objects. The Prosecco project is a research and development project aims to address the needs and constraints of small and medium enterprises by designing methods that will significantly improve BPM systems. Thanks to it sharing the conceptualization between users becomes possible, and better decision support facilities can be provided. Let me know what you think on LinkedIn or Twitter, and stayed turned for my next article that will cover best practices in embracing change with Power BI.Knowledge management in business information systems often requires a unified dictionary of business concepts, that allows for a transparent integration of such systems. An example of an error would be to use a variable/measure in the denominator for division instead of using the DIVIDE function which handles the divide by zero issue.Įxample of an error from the Best Practice Analyzer in Azure DevOps An example of a warning is leaving key columns visible in the model.Įxample of warnings from the Best Practice Analyzer in Azure DevOpsĮrrors that are encountered will fail the build, and Azure DevOps will list those errors. If warnings (as indicated with Severity Level 2 in the JSON file) are encountered, the build doesn’t fail but you will see the warnings listed in Azure DevOps.
Updated Pipeline with Best Practice Analyzer in Step 4 Now I made it simple here, because I took the pipeline template from Part 5 and added a step that runs the Best Practice Analyzer to Github. That way you start conditioning your team to check for issues that go against best practices prior to committing the Power BI dataset to Git and fix those issues.Įxample of Best Practice Analyzer and, in this case, the model has many issuesģ) Incorporate the Best Practice Analyzer in your continuous integration pipeline. And in the spirit of simplicity, others have already created JSON templates with predefined best practices such as Microsoft or Daniel Otykier.Ģ) Just like I suggested that your team use DAX Studio to test Power BI on their local machine, your team can use Tabular Editor to run your Power BI dataset through the Best Practice Analyzer.
#Bpa rules tabular editor free#
Version 2.0 of Tabular Editor is free to use, and the best practice analyzer leverages a JSON file to define rules on DAX expressions, Error Prevention, Formatting, Maintenance, Naming Conventions and more.
#Bpa rules tabular editor series#
Daniel Otykier, the author of Tabular Editor, and his blog series around Analysis Services allowed these best practices to be viable in Power BI.ġ) Leverage the Best Practice Analyzer in Tabular Editor. Best Practicesįirst and foremost, I’m standing on the shoulder of a giant here. There are linters for many programming languages, but Power BI inherently lacks a linter.
#Bpa rules tabular editor manual#
And it is further distressing that I must ask senior staff and myself to waste time (time not delivering value to customers) enforcing standardization through manual reviews and subsequent rework with updates to datasets.Ģ) There are no built-in linters - Never heard of a linter? A linter performs static analysis on your code to look for bad coding style/practices, forcing you and your team to keep a consistent code style. As a manager though, the thought of the development team building models for the same customer but have them look completely different makes me break out in hives. As a developer, I like flexibility because it allows for creativity and the ability to adjust to customer needs.
However, introducing simplicity when it comes to applying consistent design with datasets does have its challenges in Power BI: The Challengesġ) Power BI is flexible - We can name our tables, measures, columns anything we want. In short, we wanted predictability, standardization and a simple way to enforce that.ĭataOps Principle #13: We believe that continuous attention to technical excellence and good design enhances agility likewise, simplicity–the art of maximizing the amount of work not done–is essential.
And for the senior staff, we didn’t want to spend hours applying good design with manual reviews. If data analysts/customers were going to use our datasets we didn’t want them looking at two different datasets and seeing different names (DateDim, DimDate, Date Dimension, etc.) for the same dimension. We had tests, we were versioning, so progress had been made, but the naming conventions of tables and measures varied slightly. We lacked consistency… a senior developer tells me this after he reviewed some of the Power BI datasets we were building.
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