DevOps is a Process

The explicit measures podcast unpacks what does DevOps mean. It’s not a software it is a way of thinking. Matthias is the creator and inventor of our now beloved TMDL format. This conversation will blow your mind as you learn about DevOps with Microsoft Fabric.

Playlist Overview

• Total Videos: 4
• Theme: DevOps strategies and practices in Power BI
• Guest: Mathias Thierbach
• Audience: Power BI professionals and teams interested in CI/CD, DataOps, and DevOps methodologies

Videos in the Playlist

1. Deep Dive on CI/CD Branching Strategy – Ep.436
   Provides a detailed look at branching strategies for continuous integration and deployment in Power BI projects.
2. DataOps is the Future of Power BI Teams – Ep.435
   Discusses how DataOps principles can streamline Power BI team workflows and improve collaboration.
3. Top Down and Bottom Up DevOps – Ep.434
   Explores different approaches to implementing DevOps in Power BI environments, from leadership-driven to grassroots efforts.
4. DevOps and You, Your Team, and Your Data – Ep.433
   Introduces the foundational concepts of DevOps in Power BI and how they impact individuals, teams, and data governance.

Listen on Apple Podcasts

Power Query with the original Power[s]

Alex Powers is a staple in the Power BI community. Especially when it comes to working with Power Query. We had a super fun time unpacking all the rich features of Power Query with Alex in this series.

Playlist Summary

Alex Powers joins Power BI Tips to explore advanced techniques and essential skills in Power Query, aimed at maximizing performance and deepening understanding of this data engineering tool.

Videos in the Playlist

1. Max Performance in Power Query – Ep. 422
   • Duration: 1:09:00
   • Description: A deep dive into optimizing Power Query for maximum performance.
2. Power Query, Skills to Know and Learn – Ep. 421
   • Duration: 1:03:09
   • Description: Covers essential skills and knowledge areas for mastering Power Query.

Listen on Apple Podcasts

Data Science and Fabric

Tommy and Mike had a blast discussing the state of Data Science as it relates to Fabric. Microsoft is pushing into Data Engineering and Data Science by adding easy to use experiences. Learn from MVPs where you should invest your time with Fabric as it relates to Data Science.

Playlist Overview

This 4-episode series explores how data scientists can adapt to new technologies like Microsoft Fabric, the relevance of Power BI in data science, and whether traditional BI tools are being replaced. Each episode is about an hour long and offers deep insights for professionals navigating the modern data landscape.

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Video Summaries with Links

1. Education for a Data Scientist in the Age of Fabric? – Ep.418
   • Duration: 1:04:59
   • Explores how the educational path for data scientists is shifting with the rise of Microsoft Fabric and integrated analytics platforms.
2. Is Now the Time for Data Scientists to Switch to Fabric? – Ep.417
   • Duration: 1:04:10
   • Discusses the pros and cons of transitioning to Microsoft Fabric and whether it’s the right time for data scientists to make the move.
3. Should Data Scientists Care about PBI? – Ep.416
   • Duration: 1:04:16
   • Evaluates the importance of Power BI in the data science workflow and its potential to enhance data storytelling and visualization.
4. Data Science Platforms Replacing Traditional BI? – Ep.415
   • Duration: 1:01:02
   • Analyzes whether modern data science platforms are overtaking traditional business intelligence tools in functionality and relevance.

Listen on Apple Podcasts

Starting a center of excellence (COE) can feel daunting. We face political challenges. This article pushes to explore the challenges of a COE and some recommendations to handle these challenges.

The Importance of Attention to Detail

Microsoft does a great job in outlining the key aspects of COE. For more details on this topic check out the Fabric adoption roadmap found here. A summary of those items are in the list below:

• Identify the Goals for the COE, write them down share with leadership
• Outline the responsibilities and identify who should own the outcomes
• Discuss the needs for staffing and size of the COE
• Pick a COE structure, does your company work more like a centralized, a unified, federated or decentralized team.

I strongly feel that documenting the result of these conversations is a huge win. The documentation can be used to show leadership that you have a solid plan. Discussing these topics pushes towards a health data culture. Lastly, when you bring documentation to leadership you show thought of aspects that drive success.

Foundational Attributes for Success

The optics of the COE matter. COE performance and leadership are crucial, as they can impact the entire organization. Don’t underestimate the value of setting clear goals. Taking time to identify pain points with your current organization structure help with planning process for the COE.

• Setting clear goals
• Addressing pain points that you see, plan to solve those pain points
• Just start, don’t worry about making the COE perfect, plan for adjustments

Sometimes I feel that people try to over plan. Therefore, read up on the best practices provided by Microsoft’s documentation, write down your decisions then get moving! I have observed just by communicating and developing the plan really creates some momentum. Bear in mind it won’t be perfect in the first iteration. Plan on being flexible to adjust the COE to the organizations needs.

Recommendations for Overcoming Challenges

• Attention to Detail: Paying attention to aspect you can control of the COE’s performance. Engage leadership so they support the development of the COE. Remember the COE is a vote in the direction of better data culture for your company.
• Setting Clear Goals: Defining clear goals helps the team align towards a unified direction. Address pain points that could derail or distract from the creation of the COE. Connect the success of the COE to Objectives and Key Results (OKRs) outlined by the leadership team.
• Regular Communication with Executives: Regular communication with the executive team helps remove mis-aligned expectations. When you win let leadership know, they can promote your success. Success means more buy-in from the company.
• Feedback: Gathering feedback and pivot. Have empath for the process and be willing to adjust. If something is not working within the COE try something new. Ask others involved in the COE for recommendations, some of the smartest people are the ones you already work with.

For more thoughts on the COE and overcoming those challenges check out our episode on the explicit measures podcast.

Semantic link is one of the most promising technologies coming from the Microsoft Power BI and Fabric team. Semantic link has the potential to automate so many redundant tasks and tedious work. Automating and using code enables BI developers to free up time for more value-added work. Join Stephanie Bruno and Mike Carlo as they do a thorough demo of using Semantic Link.

Understanding Semantic Link

Semantic link is a powerful tool that allows direct access and manipulation of data within semantic models using code and notebooks. It offers automation, streamlined data extraction, and centralized data management within models. Throughout this workshop, we’ll delve into the diverse functionalities of semantic link and its potential benefits for data scientists, analysts, engineers, and fabric admins.

A Deep Dive into Semantic Link

This demo covers a range of topics, including:

• Accessing and visualizing a Power BI report within a notebook
• Exploring the list of reports in a workspace
• Retrieving insights about tables and columns in a semantic model
• Listing and comprehending measures within a semantic model
• Visualizing and understanding table relationships
• Utilizing semantic link for data access and manipulation

Live Demos and Practical Demonstrations

Our expert presenter, Stephanie Bruno, will lead live demonstrations and hands-on exercises to illustrate the practical applications of semantic link. The demos will encompass:

• Creating a new notebook and connecting it to a workspace
• Retrieving and visualizing reports within the notebook
• Exploring tables, columns, and measures within a semantic model
• Understanding and visualizing table relationships
• Accessing and manipulating data using semantic link
• Employing DAX magic to write and evaluate DAX expressions

The Impact of Semantic Link in Action

Throughout the workshop, we’ll showcase how semantic link empowers data scientists to access and utilize measures without the need to reconstruct complex logic. Additionally, we’ll highlight the seamless integration of semantic link with Python, facilitating efficient data manipulation and analysis.

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