Power BI - Performing Data Quality Checks Using Python & SQL

Picture this, you have a report in Power BI that someone passes off to you for data quality checks. Simple enough until they clarify they need every measure checked against the source database.
There are a few ways to make sure the measures match what is in the source data system, but you need something easily repeatable and self documenting. In this presentation, I will walk through how to use python and excel to perform our data quality checks in one batch and document any differences. In order to do that, we are going to build a python script that can run Power BI REST APIs, connect to a SQL Server, and read/write to Excel.
By the end of this session, attendees will have a plug and play tool to check data from SQL against Power BI.

Internet is required

Real-time Analytics in Fabric

As data continues to get more attention from executives and a wider range of industries, getting data in the hands of business leaders faster and more accurately has become a common request. Real-time analytics in Fabric allows us to get data streamed to a Power BI report for your end users to consume. During this session, we will go over how to create a data source in Fabric using KQL and load that data into a Power BI report that refreshes it’s visuals without needing to refresh the browser. We will also look at how to create an event stream as a data source using publicly available data.

Power BI - How to Fix an Inherited Report

A common request in the realm of reporting is "hey, we have this report that a previous VIP used, but the report is really slow and the new VIP would like to revamp the report to answer their questions". Great, you think, I can knock this out no problem! Then, you open the report. Fifty tables, over a hundred measures, and six calculated tables later you start to panic.
This session will go into best practices for dissecting a complicated report and a checklist for quick wins. No need to panic, this session will help build a toolbelt to tackle any reconstruction project.

Power BI Meets Programmability – TOM, XMLA, C#, and TMDL

It is rare to find a Power BI developer who has a background in C#, but C# and other programming languages offer a lot of automation and scalability that is lacking in Power BI. XMLA is a powerful tool available in the online Power BI service that allows report developers to connect to their data model and adjust a variety of entities outside the Power BI Desktop application. For example, the XMLA endpoints can be used within a pipeline triggered by an application to update a Power BI model schema. This allows end users to create custom UDFs (user defined fields) on the fly and delete them. Similarly, developers can create and use translations for customer specific column renames without worrying about breaking visuals and complicated data models.

Introduction to Fabric Shortcuts

With the release of Fabric and OneLake, Microsoft has made integrating data sources from various cloud systems a breeze. During this session, we will walk though how to create shortcuts for both Azure Data Lake Storage gen 2 and AWS S3. After creating the shortcuts, we will walk through connecting the shortcuts to a Power BI report and build relationships between the two cloud sources. All without the crazy web of connection hoops to jump through and without creating copies of the data. In addition to connecting Power BI to the shortcuts, we will also connect real-time analytics and SQL and demonstrate how to query via the shortcut.

Bite Your Gold (Layer) - Data Quality Checks in Fabric Notebooks between Power BI & SQL

Picture this, you have a report in Power BI that someone passes off to you for data quality checks. Simple enough until they clarify they need every measure checked against the source database.
There are a few ways to make sure the measures match what is in the source data system, but you need something easily repeatable and self-documenting. In this presentation, I will walk through how to use python notebook in Fabric to perform our data quality checks in one batch and document any differences. In order to do that, we are going to build a python script that can run Power BI REST APIs, connect to a SQL Server, and read/write to a logging table.

Power BI - Time to Git CI/CD

As report developers, requirements for reports are constantly evolving which leads the reports themselves to change. However, inevitably, there will be one party who uses that report and was unaware of changes. They may request a list of changes or want that report rolled back to a previous version. With git, we can provide a list of changes and roll back to any version requested.

Taking a page from application developers, Power BI developers can now develop reports in branches and merge changes using git and Azure DevOps! If half those words are new to you and you’re looking for a better way to manage version history, come to this session! We will go through the basics of git including what branches are, how to use them, and some best practices while working with git. Git has a few tricks that we will go through as well like fetching a branch, in-line comparisons, and pulling to avoid merge conflicts. Don’t worry, we will also touch on how to resolve merge conflicts as well as how to avoid them. After the basics of git, we’ll walk through how to use git with Power BI workspaces and Azure DevOps.

Power BI - Creating a M-agical Date Table

By the end of this session, attendees will have a deeper understanding of how to manipulate M code in Power BI through the process of building a highly customized date table.

Many times Power BI developers get caught between DAX and SQL when they need the flexibility of DAX but the performance benefits of SQL, which leaves them in the land of M. Similar to Oz, M is full of magic that is more attainable than meets the eye. This session demystifies the functionality of Power Query and covers when Power Query may be the best option.