Details
Our agenda is live!
📅 Saturday, September 26, 2026 📅
🕜 8:00 am
⭐️ SUPERCHARGE YOUR DASHBOARDS WITH POWER BI
We’ll explore advanced techniques for optimizing data models, applying DAX effectively, enhancing visual storytelling, and integrating AI features within Power BI. You’ll also learn performance tuning strategies and best practices for governance and deployment in enterprise environments. Whether you’re an analyst looking to level up your skills or a data professional managing enterprise BI, this session will give you practical techniques to supercharge your dashboards.
Data Model Optimization: Designing efficient star schemas, relationships, and aggregations for faster queries.
Advanced DAX: Practical patterns for time intelligence, KPIs, and scenario analysis.
Performance Tuning: Techniques to speed up refreshes, reduce report load times, and improve user experience.
AI in Power BI: Leveraging features like Smart Narratives, Q&A visuals, and Azure ML integration.
Storytelling Best Practices: Moving beyond charts to create dashboards that guide decisions.
Deployment & Governance: CI/CD pipelines, workspaces, and governance strategies for enterprise-scale Power BI.
🙂 Shubhangi Goyal
🕜 9:00 am
⭐️ BRONZE, SILVER, GOLD: AN OPINIONATED GUIDE TO MEDALLION ARCHITECTURE IN FABRIC
The medallion architecture has become the go-to pattern for organizing data in a lakehouse, but ask five people how to implement it, and you'll get seven different answers. Where does the business logic go? What belongs in silver? Should bronze even have Delta tables? If you've ever felt confused by contradictory advice on how to layer your data platform, this session is for you.
In this session, I'll walk you through how I implement the medallion architecture in Microsoft Fabric and more importantly, why. This is not a "it depends" kind of talk. I have strong opinions on what each layer should and should not do, shaped by years of building production data platforms. I'll share those opinions openly, explain the reasoning behind them, and show you what this looks like in practice inside a Fabric lakehouse.
We'll start at the bronze layer, where raw data from APIs, databases, and files lands in the Files section of a lakehouse before being structured into Delta tables. Then we'll move to the silver layer — and this is where my approach breaks from the crowd. No business logic. No transformations. No aggregations. Silver is a faithful, versioned, schema-managed mirror of your source systems, enriched only with lineage and system fields to support time travel and schema evolution. Finally, we'll arrive at the gold layer, where PySpark notebooks (or stored procedures) transform silver data into dimensional models following the Kimball methodology: ready to power Power BI semantic models, AI data agents, and beyond.
Whether you're a Power BI professional stepping into data engineering, a data engineer exploring Fabric for the first time, or someone migrating from another platform, you'll walk away with a clear, practical, and opinionated blueprint for structuring your data lakehouse. A basic understanding of data warehousing concepts and a general awareness of what Microsoft Fabric is will help you get the most out of this session.
After attending, you will be able to: explain the purpose and responsibilities of each medallion layer; design a bronze layer that separates raw file ingestion from Delta table structuring; implement a silver layer focused on source fidelity, schema evolution, and time travel — without business logic; build a gold layer using notebooks and stored procedures to deliver Kimball-style dimensional models; and make deliberate architectural choices about where logic belongs in your data platform.
🙂 Bas Land
🕜 10:00 am
⭐️ CHOOSING THE AZURE SQL DB TIER, TALES FROM THE TRENCHES
Azure SQL Databases, databases as a service. You've heard all the tales and read all the marketing materials. Are you planning to migrate workloads from on premises to Azure? Or have you migrated your workloads but are you in doubt if you've made the correct choice?
In this session, I'll walk you through the different options that are available and tell you about the experiences I've had when working with them. I've been involved in a number of projects and most tiers and SKU's have been tried and tested. Not all and before you know it, there are new ones.
Some were successful, some weren't but all depending on their loads and characteristics. To give each database a fair chance, I've been using a set of scripts to get a good comparison. A script that I will share with you, along with my way of using that.
Azure SQL DB performance comes at a price, so I'll cover the costs as well to give a complete picture.
When you leave the session, you'll have a better understanding of the Tier and SKU differences and a starting point on how to test it for your workload.
🙂 Reitse Eskens
🕜 11:00 am
⭐️ INSIDE THE VERTIPAQ ENGINE
The VertiPaq engine used by SQL Server Analysis Services Tabular, Power BI, and Power Pivot, is a columnar database capable of incredible performances, both in speed and compression ratio. In this session, we will perform a deep dive in the internals of the database architecture, discovering how Vertipaq stores information, in order to gain better insights into the engine and understand the best way to model your data warehouse to leverage the features of VertiPaq. We will show common and useful techniques to increase the compression ratio and obtain better performances from your Tabular data model.
🙂 Marco Russo
🕜 12:00 pm
⭐️ THE FELLOWSHIP OF THE STAR SCHEMA: TRANSFORMING OLTP DATA FOR POWER BI
One does not simply build reports on OLTP data. Join us on an epic journey from the depths of raw, normalized tables to the shining halls of a well-modeled star schema fit for Power BI greatness. We will demystify the differences between OLTP and OLAP, walk through star schema vs snowflake (and why Power BI has a clear favorite), and show you the compression differences between normalized and columnar storage that will make you never look at an OLTP model the same way again.
We will cover the different types of dimensions, when to use them, and how to handle Slowly Changing Dimensions without losing your mind. You will leave knowing exactly how to identify a bad model, how to convert OLTP data into a clean star schema, and how to forge relationships that rule them all.
Whether you are a data wizard, a curious hobbit, or somewhere in between, your models will never be the same
🙂 Kristyna Ferris
🕜 1:00 pm
⭐️ ASK, DON'T BUILD: THE COACHING QUESTIONS THAT SAVE YOUR POWER BI PROJECT
Most analytics projects don't fail in Power BI Desktop.
They fail three weeks earlier, in a meeting where someone says: “We just need a simple sales dashboard.”
Everyone nods.
The data team starts modelling.
Three sprints later, the fact table is being rebuilt because the real problem was never a dashboard. A commercial leader needed to decide which customers required intervention before the end of the month.
This workshop is about the conversation before the architecture.
Using questioning patterns inspired by professional coaching, participants will practise how to slow down the rush to solution without turning requirements gathering into a three-hour interrogation.
We will work with realistic analytics requests and practise how to:
- distinguish a requested artefact from the decision it is supposed to support,
- contract for what “success” means before discussing visuals,
- surface assumptions without accusing the stakeholder of being wrong,
- explore multiple perspectives when business and engineering use the same word differently,
- translate the resulting clarity into model grain, metrics, and semantic design.
This is about learning to ask questions that create shared understanding before technical decisions become expensive.
🙂 Joanna Hryniewicz
🕜 2:00 pm
⭐️ POWER BI MEETS GITHUB: AUTOMATING CI/CD WORKFLOWS AND COLLABORATION
Ever struggled with version control in Power BI?
How do you test new features, track changes, or roll back updates while maintaining stability?
With Power BI Project files (PBIP), the TMDL format, and GitHub integration, developers now have access to structured version control, collaborative workflows, and CI/CD automation.
In this session, I’ll share real-world insights from a five-member BI team, covering the benefits, challenges, and limitations of this evolving workflow. We’ll explore how GitHub integration streamlines version control, testing, and deployment.
Beyond version control, I’ll also demonstrate how GitHub Actions can automate code reviews, enforce best practices using the Best Practice Analyzer, and auto-format code, ensuring consistency across projects.
To wrap up, I’ll showcase these solutions in action with a live demo.
Who should attend? Power BI developers, BI team leads, and data professionals at an intermediate to advanced level looking to optimize workflows and adopt best practices in collaborative development.
🙂 Daniel Patkos
🕜 3:00 pm
⭐️ AN INTRODUCTION TO SQL SERVER INDEXES AND STATISTICS
One of the most amazing features of SQL Server is how flexible it just works with your queries. One of the most challenging features of SQL Server is that it lets you run just about anything, regardless of efficient query or underlying table structure. Database indexing can tune the SQL Server engine to more closely align with how your organization uses the database. It is as much an art as it is a science, but come learn how to learn the science of how to measure and monitor index usage patterns so you can become a SQL Server indexing master and learn how to make your applications run faster with little to no query or code changes. With a free script and software package, we will learn how to learn how to find the higher consumers, and review a query’s execution plan to hunt for inefficiencies.
🙂 David Klee
🕜 4:00 pm
⭐️ SEMANTIC LAYER IS BACK, IT NEVER LEFT: WHAT AGENTIC ANALYTICS CAN LEARN FROM DECADES OF BI
The semantic layer is back. Again. Apparently, we misplaced it somewhere between the self-service BI revolution, the data lake, the lakehouse, and the metrics store. Business meaning is now being announced as the next great innovation—with the arrogance of explorers claiming to have discovered a continent where BI teams have lived for decades.
Names change, but the problem predates current products. Kimball and Inmon made structure deliberate. BI added measures, hierarchies, relationships, security, and business vocabulary; Microsoft later called one generation BISM and built it on VertiPaq. Semantics were never perfect, but the rule was clear: business meaning must be designed and maintained.
Data Agents now make that rule visible again. Fluent is not necessarily correct. Without grain, governed measures, time intelligence, and shared definitions, an agent can only improvise more confidently. MCP can expose a contract; it does not create one. A large language model can use a KPI; it should not quietly redefine it.
This session uses the current agentic wave to revisit durable BI fundamentals, with a little historical perspective and a healthy amount of skepticism. The goal is not to resist new interfaces. It is to stop rebuilding old semantic mistakes behind a more conversational user experience.
🙂 Jean-Pierre Riehl
🕜 5:00 pm
⭐️ PARAMETER SNIFFING: EVERYTHING YOU HAVE BEEN TOLD IS WRONG!
Execution plan reuse is one of the most misunderstood aspects in any relational database. While it serves us well 99.99% of the time, it is very easy to trip up when parameter sniffing results in performance problems.
In this session, we will thoroughly dissect how plan reuse works, discuss parameter sniffing, and the many potential solutions available to resolve it. We will examine the many poor solutions that are found on blogs and web pages all over the internet and why applying a quick-fix may result in long-term performance headaches if not thoroughly researched prior to implementation.
🙂 Edward Pollack