Author: Paul Turley

Back in September 2025, about four months ago, I made this earlier post, as we were just gaining early experience…

This post is part of a series of excerpts from my forthcoming book “Microsoft Power BI Data Analyst Associate Study…

My new book “Microsoft Power BI Data Analyst Associate Study Guide” from O’Reilly Press is really two books in one. When O’Reilly approached me about writing a study guide for the PL-300 exam, one of the most popular Microsoft certification exams, I said that I wanted to be a two-part book to help exam candidates join the thriving Power BI community followed by a guide for using Power BI in real-world applications in accordance with industry best practices. The second half of the book is focused on how to apply best-practice design after learning the Power BI building blocks. Some of the content in the second half of the book is updated and elaborated material from earlier blog posts and 2025 posts I will reference in this blog series. As of this post in January 2026, the online book is available as an early release through the O’Reilly subscription service and scheduled to be delivered through booksellers like Amazon early this year.

I had the pleasure of hosting eight hours of two-hour-long study group sessions for the Microsoft-sponsored Fabric Data Days in November and December. The event was coordinated and promoted by the Fabric and Power BI Community. As a group, we reviewed live demonstrations and preparation materials for the DP-600 Data Analyst Engineer exam. I’m proud to say that all of the attendees who participated in the four sessions have since passed the exam!
Whether you or your employer are interested in certification, Microsoft have done a remarkable job identifying three important roles related to BI and analytical reporting projects. There are three certification exams that effectively define the roles associated Power BI and the Fabric data platform.
Enterprise BI Solutions Need Analytics Engineers…

How do Copilot and Agentic AI change the way business users will interact with organizational data? Is the platform ready and are we ready to embrace it? AI agents and assistants are part of our daily lives. LLM-driven agents like Alexa, Siri, Gemini and Grok provide entertainment, perform requested tasks and answer questions with varying degrees of accuracy and reliability. Sometimes they misunderstand and provide out-of-left-field responses for simple questions. I might ask: “Show me the best practices for my model.” and get: “Of course! Here’s a detailed guide on runway walking techniques and how to maintain a confident posture during a fashion show.”

This series of posts will focus on how to prepare Microsoft Fabric and Power BI semantic models to support effective self-service reporting and analysis using Copilot.

My Dad and I did a lot of fishing when I was young. Back then, we fished for wild trout…

This post is part of a series of excerpts from my forthcoming book “Microsoft Power BI Data Analyst Associate Study…

It is no secret that the AI revolution has started. Most every modern business application has an agent, a copilot or assistant asking to help write a document or take meeting notes. Nearly every banking or telecom customer service site greets us with a chatbot. Microsoft Teams meetings are concluded with Copilot summarizing the discussion and talking points. Like Chat GTP, Copilot serves as a next-level search engine that can spit-out code snippets and summaries from blog posts and product documentation, but what’s next? When can I say “hey, Copilot, gather up data from our company line of business databases and forecast next year’s profitability by month and region.” Will AI reach that level of sophistication? Before we get into AI, let’s cover a topic that is super basic and super important in this context.

Fabric Solution Architecture and Power BI…

Both Azure DevOps and GitHub are supported Git hosts for Power BI and Fabric workspace integration. I will demonstrate using GitHub rather than Azure DevOps because, it is free for personal use. Getting started with GitHub is quite easy, and anyone can have a free GitHub account. Simply navigate to http://GitHub.com and sign-up for an account. You may create a personal or organizational account, and your personal account can be associated with multiple organizations.

After you have an account, sign-in and create a new repository (repo). A repo may be public or private and can, optionally, be part of an organization. This is the Create a new repository page in GitHub.

Each repo generates a URL with the owner or organization name and the name of the repo. Since I created this as a public repo, you can access it by navigating to http://GitHub.com/paulturley/AirlinePerformance. It’s as easy as that!

GitHub provides excellent documentation and support resources on the website and I recommend that you familiarize yourself with these resources to learn about basic functionality and site navigation. We only need basic functionality so don’t get lost in the details.

For the business intelligence professional, DevOps can be a confusing topic because it intersects with many disciplines and philosophies. I’m hopeful that a bit of reflection on my own experience over the years provides some valuable insight. I have seen DevOps and version control implemented on dozens and perhaps hundreds of projects, with different degrees of sophistication and control.

If you work in a software development environment where DevOps is practiced as part of your team’s development culture, you know that DevOps truly is a holistic methodology, including practices can be very extensive and regimented. Software development tends to be a linear process, whereas data and BI projects are more iterative in nature. Frankly, DevOps purists can be down-right militant about enforcing all the rules, which many BI specialists tend to skirt, in an effort to move quickly.

If you are a business intelligence analyst or developer and suddenly find yourself working on a team where DevOps is practiced, you will likely find the process to be more strict and less flexible than typical ad hoc BI development. The key is to strike the right balance for your organization’s needs.

There is certainly a flavor of DevOps that seem to be over-engineered and counter-productive. DevOps practices exist because they address critical needs in a software development environment, but you should find the right balance for your organization’s project needs. Be mindful that the very purpose for business intelligence is to deliver insights and reporting insights directly to business users, which entails quick iterations through the entire process – from requirement refinement to report delivery, with several steps in-between.

I just read that the Miss Universe contestant from Panama was evicted from the Miss Universe pageant. I don’t know…