Fabric Data Agents & Power BI: the future of self-service BI? en nl

With the arrival of Copilot and Data Agents in Microsoft Fabric, your semantic model becomes much more than just the foundation for reports. It turns into the brain of an AI assistant that can generate insights, answer questions, and even take actions on its own.

In this demo-driven session, you will learn:
- What Fabric Data Agents are and how they leverage your Power BI semantic model
- How to make a model AI-ready with clear naming, descriptions, and relationships
- Best practices to help Copilot in Power BI deliver more accurate answers and insights
- Examples of AI interactions that go beyond visuals and DAX

Getting Started with Data Science in Microsoft Fabric en

AI and Machine Learning are very hot topics at the moment. Discover how to get started with Data Science in Microsoft Fabric. No need for a degree in Math!

We’ll explore data with scatter plots and correlation matrices, explain regression vs classification, and show how AutoML with FLAML makes building models simple and fast. By the end of this workshop, you’ll have trained and tested models inside Fabric and know how to apply them in real-world analytics projects using Power BI and OneLake.

Python in Fabric for Data Engineers: all you need to know en

The world of data engineering is changing. Being fluent in T-SQL is not enough anymore. Whether you want to implement a Lakehouse in MS Fabric, use Jupyter Notebooks to analyse your data or built an ETL pipeline, Python is the new kid on the block.

In this demo filled session I will get you up to speed with Python and learn you all the tips and tricks you need to know. The following topics will be covered:
- Syntax overview
- Leverage Jupyter Notebooks in Fabric
- Data Wrangling with Pandas
- Select, Group, Filter and Join data
- Big Data with PySpark
- Extract data from SQL Server, CSV files and Parquet files
- Load data into a Lakehouse Parquet files and Delta Tables

Nieuwe DAX ontwikkelingen en

Microsoft heeft aanzienlijke vooruitgang geboekt in de ontwikkeling van DAX, met de toevoeging van diverse krachtige functionaliteiten. In november 2023 introduceerde Microsoft een nieuwe feature in Power BI: de DAX Query View. Deze vierde view maakt het mogelijk om DAX Queries rechtstreeks in Power BI te creëren en te testen, een functie die voorheen alleen mogelijk was met externe tools zoals DAX Studio of Tabular Editor. Ontdek hoe deze tool het analyseren van data en het schrijven van DAX vereenvoudigt.

Een andere significante verbetering in Power BI Desktop is de mogelijkheid om Calculation Groups te maken. Dit kan het aantal Measures in je model drastisch verminderen, omdat je veelgebruikte logica zoals Time Intelligence (bijvoorbeeld YTD, groei ten opzichte van vorig jaar) nu slechts één keer hoeft te definiëren.

Bovendien zijn er nieuwe Window functies aan DAX toegevoegd, zoals INDEX, OFFSET en WINDOW. Het beheersen van deze functies vereenvoudigt het schrijven van DAX in diverse scenario’s en kan tevens de performance van je rapporten verbeteren.

Kortom, een praktische, hands-on deep dive waarmee je het schrijven van DAX beter onder de knie krijgt.

Financiële rapportages in Power BI met standaard visuals nl

Financiële rapportages maken in Power BI kan erg uitdagend zijn omdat je vaak moeilijke DAX formules nodig hebt en de mogelijkheden van de standaard visuals erg beperkt zijn.

In deze deepdive zal ik jullie meenemen in de meest voorkomende challenges die je tegen komt en hoe je die op kunt lossen.

Onderwerpen die aan bod zullen komen:
01 Financial Statement P&L
- Challenge: Chart of Accounts Hierarchy + Sorting
- Actual v.s. Budget
- Challenge: Calculated Values/Ratio's

02 Time Intelligence
- Challenge: Filter current Year/Quarter/Month
- DAX Time Intelligence top 5 functions

03 Financial Statement Balance
- Challenge: Closing Balance Semi Additive LAST DATE
- Challenge: Ragged Hierarchy
- Challenge: Missing data

04 Time Intelligence Advanced (Forecasting)
-Challenge: Forceast linear
-Challenge: Forecast with seasonality

Python for Data Engineers: all you need to know en

The world of data engineering is changing. Being fluent in T-SQL is not enough anymore. Whether you want to implement a Delta Lake using Data Bricks, use Jupyter Notebooks to analyse your data or built a ETL pipeline in Microsoft Fabric, Python is the new kid on the block.

In this demo filled session I will get you up to speed with Python and learn you all the tips and tricks you need to know. The following topics will be covered:
- Syntax overview
- Leverage Jupyter Notebooks in Fabric
- Data Wrangling with Pandas
- Select, Group, Filter and Join data
- Big Data with PySpark
- Extract data from SQL Server, CSV files and Parquet files
- Load data into a Lakehouse (MS Fabric) CSV + Parquet files and (Delta) Tables

PreCon Power Query Advanced (Editor) en nl

Do you already have the necessary experience with Power Query but want to take it a step further? Then this hands-on workshop is for you!

Together we will dive into the following topics:
- Introduction to M formula language: Datatypes, Functions
- Custom Functions: how to create and use them
- File/Folder import from SharePoint/MS Teams
- Error Handling: how to deal with them and how to report them
- Privacy Levels: what do all those strange error messages mean?
- Data combining (Merge Query) from multiple sources
- Data import from Web APIs
- Performance Tuning
- Other tips and tricks

Are you not afraid to get your hands dirty and explore the Advanced Editor? Then sign up for this workshop!

Bring your laptop with you because we will also get the opportunity to play with the examples given.

Full day PreCon

Power BI Performance Tuning like a Pro en nl

Did you ever had a Power BI report that was very slow? In this session full of demos, I'll show you the techniques you can use to make any report lightning fast.

Topics include:
- Report optimalizations: Visuals, Performance Analyzer
- Formula Engine (DAX) tuning : CALCULATE/FILTER, Calculated Columns, Measures + Execution Plans
- Power Query: Direct Query v.s. Import , Queryfolding, Dataflows, Incremental Refresh
- Storage Engine (Vertipaq) optimalization
- Data model best practices: Star Schemas, Aggregations

Power BI Performance Tuning like a Pro en nl

Heb jij ook wel eens een rapport dat niet vooruit te branden is? In deze sessie vol demo's laat ik je zien welke technieken je kunt gebruiken om ieder rapport razendsnel te maken.

Aan bod komen onderwerpen als:
- Import (SharePoint Folder, Parameters + DataFlows
- Import Web API + Incremental Refresh
- Queryfolding, Direct Query v.s. Import + Aggregations
- Data model, Star schema's, relationship types
- Storage Engine (Vertipaq)
- Formula Engine (DAX): CALCULATE/FILTER, Calculated Columns, Measures

Develop Custom Visuals in Power BI with D3.js en

Hard to create good looking data driven visuals with JavaScript? Not anymore!

Power BI uses a visualization engine based on D3, which stands for Data-Driven Document. D3.js is a JavaScript library to produce sophisticated, interactive, dynamic data visualizations using modern web-based technologies. As the D3.org website states: "D3 helps you bring data to life using HTML, SVG, and CSS."

This library combined with the Power BI modelling and calculation engine gives you immense power to create your own Custom Visuals with TypeScript and Visual Studio Code.

Power BI Security Best Practices en

How do you make sure that your data can be shared securely? What options are available to show only the data that a user is allowed to see.

In this session full of demos, you will learn when and how to use App Workspace Roles, Apps and (Dynamic) Row Level Security.

Forecasting with DAX en

Would you like to predict where your sales or expenses will end up at the end of the year and also visualize this in Power BI, then this session is for you!

With the help of demos, I will show you how you can extrapolate your figures with DAX formulas in a number of ways:
- Linear regression
- Extrapolation based on seasonality
- Using Targets, Budgets or Forecasts as future values and adding them to the actuals.

Power Query Advanced (Editor) en nl

Heb je al de nodige ervaring met Power Query maar wil je een stap verder dan is deze hands-on workshop voor jou!

We duiken samen in de volgende onderwerpen:
- Introductie formuletaal M: Datatypes, Functies
- Custom Functions: hoe maak en gebruik je ze
- File / Folder importeren vanuit SharePoint / MS Teams
- Error Handling: hoe ga je hier mee om en hoe rapporteer je ze
- Privacy Levels: wat betekenen al die rare foutmeldingen?
- Data combineren (Merge Query) vanuit meerdere bronnen
- Data importeren vanuit Web API's
- Performance Tuning
- Overige tips en trucs uit de praktijk

Ben je niet bang om je handen vies te maken en in de Advanced Editor te duiken schrijf je dan ik voor deze workshop!

Neem je laptop mee want je mag zelf ook aan de slag.

Import Web API data using Azure Data Factory en

Today, a lot of data is in SAAS solutions in the cloud. Like SalesForce, Exact Online and Dynamics 365. To import this data using Azure Data Factory you need to connect to the data via a Web API (Web Service). This can be challenging if you have never done this before.

In this session I will show you step by step what to do and what pitfalls to avoid. The following topics will be covered:
- Introduction to Web APIs: JSON/XML data format, REST API + ODATA
- Paging: often you can't get all the data at once and you have to call the Web API several times, page by page.
- Variables and ForEach activity in Data Factory
- Incremental Refresh: since fetching all data can be very slow via a Web API, it is better to only load new data.
- Authentication using API Token or OAuth (Access Token)

Power BI Datamart introductie nl

SQL Server is terug! Power BI Datamarts
is een self-service oplossing, waarmee business gebruikers (en BI Professionals) data kunnen verzamelen in een echte Azure SQL Database binnen de context van een Power BI App Workspace.

De gegevens worden in deze Datamart geladen met behulp van Power Query Dataflows. Hiermee kun je de huidige spaghetti van rondzwervende Datasets en Dataflows vervangen door 1 Datamart per domein!

In deze met demo's gevuld sessie laat ik zien hoe dit allemaal werkt, wie de doelgroep is en wanneer je dit zou kunnen gebruiken.

Introduction to Power Query Language M en nl

M is the formula language behind Power Query. Every transformation you apply to your data is translated into M code. 

Knowledge of this language opens doors that remain closed when you only use the graphical interface.

In this session you will learn what else is possible by using the Advanced Editor, such as:
- Working with variables and data types
- Custom Columns and Time Intelligence Filtering
- Data grouping and sorting
- Creating table and scalar functions
- Crossjoin between 2 tables (make all possible combinations)

Recently, the Advanced Editor also has Intellisense, so now is the time to delve into M!

I gave this presentation at SQL Bits 2020.
Slides:
https://arthurgraus-my.sharepoint.com/:p:/g/personal/agmgraus_arthurgraus_nl/ERkp1u5dh-tKgXa6cq6MjfIBYyiQE6B-0SzmsFX4KFlfCg?e=mmvf5Z
Video:
https://ilovepowerbi.tips/2020/08/31/introduction-to-power-query-language-m/

Error handling like a pro in Power Query en nl

How do you prevent unexpected data or incorrect files from blocking your dataset refresh?

In this session you will learn what options you have to make your Power Query ETL more robust, such as:
○ catch errors with try otherwise and error details
○ filtering out and logging invalid files
○ filtering and logging incorrect rows
○ Creating error reports

I gave this presentation at the Dutch Power BI Gebruikersdag 2020 (PBIG).

Slides + demo files:
https://arthurgraus-my.sharepoint.com/:u:/g/personal/agmgraus_arthurgraus_nl/EWjNQUoDgPNImrIt9gdKkZsBDnzCvbXCjnrT8N05RIOuNQ?e=Vg6nro

Master Class DAX en

Ben je op zoek naar meer diepgang op het gebied van DAX formules en wil je exact het verschil leren tussen de Row Context + Filter Context dan is deze 1 daagse Masterclass DAX voor jou.

In deze training leer je geavanceerde DAX formules maken in zowel Calculated Columns als Measures om nog meer inzichten uit je data te halen. Onderwerpen:
- Verschil tussen Row context en Filter context.
- Calculated Columns en Measures wanner gebruik je wat?
- Waardes opzoeken in andere tabellen: LOOUPVALUE, RELATED + RELATEDTABLE
- Filter Context aanpassen met: CALCULATE + FILTER
- Time Intelligence functions: PREVIOUSMONTH, PARALLELPERIOD, SAMEPERIODLASTYEAR + DATESYTD
- Custom Time Intelligence Template o.b.v. DATESBETWEEN: Moving Average + Extrapolatie

Error handling like a pro in Power Query en nl

Hoe voorkom je dat onverwachtte data of foutieve bestanden het verversen van je dataset blokkeert?

In deze sessie leer je welke mogelijkheden je hebt om je Power Query ETL robuuster te maken, zoals:
○ fouten afvangen met try otherwise en fout details
○ ongeldige files uitfilteren en loggen
○ foutieve rijen uitfilteren en loggen
○ foutenrapportage maken

Introduction to Power Query Language M en nl

M is de formuletaal achter Power Query. Elke transformatie die je toepast op je data wordt vertaald naar M code. 

Kennis van deze taal opent deuren die als je alleen de grafische interface gebruikt gesloten blijven.

In deze masterclass leer je wat er allemaal nog meer mogelijk is door gebruik te maken van de Advanced Editor, zoals:
- Werken met variabelen en data types
- Custom Columns en Time Intelligence Filtering
- Data groeperen en sorteren
- Geavanceerde tabel en scalar functies maken
- Crossjoin tussen 2 tabellen (alle mogelijk combinaties maken)

Sinds kort heeft de Advanced Editor ook Intellisense, dus nu is het moment om je te verdiepen in M!

Call Web APIs like a pro in Power Query en nl

Today, a lot of data is in SAAS solutions in the cloud. Like SalesForce, Exact Online and Dynamics 365. To use this data in Power BI you need to connect to the data via a Web API (Web Service). This can be challenging if you have never done this before.

In this session I will show you step by step what to do and what pitfalls to avoid. The following topics will be covered:
- Introduction to Web APIs: JSON/XML data format, REST API
- Paging: often you can't get all the data at once and you have to call the Web API several times, page by page.
- Custom Functions in M: the building blocks of every Web API call
- Incremental Refresh: since fetching all data can be very slow via a Web API, it is better to only load new data.
- Authentication using API Token or OAuth (Access Token)

To give you an impression of this presentation:

https://arthurgraus-my.sharepoint.com/:u:/g/personal/agmgraus_arthurgraus_nl/ERqDpvMwDL5AlaU4oL2HYlgB633bFxF4iVjVZ56Bj7hagw?e=MUEK7r

Web API’s aanroepen vanuit Power BI en nl

Tegenwoordig zit veel data in SAAS oplossingen in de cloud. Denk aan SalesForce, Exact Online en Dynamics 365. Om deze data te gebruiken in Power BI moet je connectie leggen met de data via een Web API (Web Service). Dit kan uitdagend zijn als je dit nog nooit gedaan hebt.

In deze sessie laat ik je stap voor stap zien wat je moet doen en welke valkuilen je moet vermijden. Aan bod komen de volgende onderwerpen:
• Introductie Web API’s: dataformaat JSON/XML, REST API
• Paging: vaak kun je niet alle data in een keer op halen en moet je de Web API meerdere keren aanroepen pagina voor pagina.
• Custom Functions in M: de bouwblokken voor iedere Web API call
• Incremental Refresh: aangezien het ophalen van alle data erg langzaam kan zijn via een Web API is het aan te raden om alleen de nieuwe data in te laden.
• Authenticatie m.b.v. API Token of OAuth (Access Token)

Getting started with Data Science in Microsoft Fabric + Power BI en

AI and Machine Learning are very hot topics at the moment. In this workshop, I'll introduce you in the world of Data Science within Microsoft Fabric.

We'll delve into:
- Exploratory Data Analysis: Understand the correlation between various data columns in your dataset. We'll visualize these with scatter diagrams, histograms, pair plots, correlation matrices.
- Introduction to Machine Learning: Distinguish between Classification vs. Regression Models. We'll also discuss test, validation, training data, model performance, and AutoML.
- Regression Models in Microsoft Fabric with Python library Sci-kit Learn: Predict values such as revenue, stock, or house prices.

Getting started with Data Science in Microsoft Fabric en

AI and Machine Learning are very hot topics at the moment. In this workshop, I'll introduce you in the world of Data Science within Microsoft Fabric. The best part? No need for an advanced degree in Math!

We'll delve into:
- Exploratory Data Analysis: Understand the correlation between various data columns in your dataset. We'll visualize these with scatter diagrams, histograms, pair plots, correlation matrices.
- Introduction to Machine Learning: Distinguish between Classification vs. Regression Models. We'll also discuss test, validation, training data, model performance, and AutoML.
- AutoML with FLAML (Fast Library for Automated Machine Learning): an easy way to get started with Machine Learning without having to know al the algorithms.
- Regression Models using Auto-ML: Predict values such as revenue, stock, or house prices.
- Classification Models using Auto-ML: Determine classes, like identifying prospects most likely to make a purchase.

Cheapskate’s Guide to Fabric: Big Data, Small Wallet en

💰 Want the power of Fabric without the Fortune 500 budget? You’re in the right place. This session is for cheapskates, budget warriors, and anyone trying to squeeze every last drop out of their Fabric environment without getting burned by cloud costs.

We’ll cover real, practical ways to save money, including:
✅ Automating Fabric shutdown with Python & Azure Automation (because idle resources = wasted cash)
✅ Running Workbooks via the Fabric API
✅ Fabric Capacity Survival Guide – How to scale just enough, but not too much
✅ Spark Clustering on a Budget – how to setup distributed computing without setting your wallet on fire

Expect live demos, cost-saving hacks, and Python magic—because the best Fabric setup is one that's up when you need it and shuts down when you don’t.

I work al lot with smaller companies and freelance consultants that don't have the budget big companies have.

For them it can be challenging to get started with Fabric. In this session they will learn some nice tricks to keep the cost low using some practical Python script that they can start using right after this session.

Warm regards,

Arthur Graus

Parallel Power: Supercharging Fabric with Spark’s Distributed Engine en

Tired of slow, loop-based data processing that takes forever? Whether you're dealing with paginated API calls, massive datasets, or inefficient sequential workflows, it's time to harness the full power of Apache Spark in Microsoft Fabric.

This session is your fast-track introduction to Spark, covering its distributed nature and how it can massively speed up your Python workloads. We'll break down the fundamentals of Spark’s execution model and explore how to parallelize tasks intelligently instead of waiting on slow loops. You'll learn how to:

✅ Understand how Spark distributes work across clusters
✅ Convert synchronous Python loops into async distributed processing
✅ Efficiently handle large-scale data pipelines and API calls
✅ Optimize Spark’s performance for maximum speed and efficiency

Through real-world examples and hands-on insights, you'll leave this session equipped to transform your slow scripts into lightning-fast distributed workflows.

Forecasting in Power BI met Python nl en

DAX is een krachtige taal voor data-analyse in Power BI, maar wanneer het aankomt op forecasting loop je al snel tegen de grenzen aan. Gelukkig biedt de Python integratie in Power BI / Power Query hier uitkomst.

In deze technische deepdive leer je hoe je forecasting modellen kunt maken met behulp van Python.

Je leert onder andere:
- Hoe je Python integreert in Power Query
- De basisprincipes van Time Series Forecasting: trend, seizoensinvloeden en residuals
- Het belang van stationariteit
- Klassieke modellen zoals: Differencing, Autocorrelatie, AutoRegressie (AR), Moving Average (MA), ARIMA en SARIMA
- Moderne tools zoals Facebook Prophet

Advanced dimensional modelling en

Are you already familiar with Power BI, but having trouble modeling data in Fact and Dimension tables? Then this deep-dive is for you!

In this session full of demos and practical examples, the following topics will be covered:
- Dimensional modeling: Dimensions, Facts + Snowflakes
- Working with multiple Fact tables
- Many-to-many relationships
- Date and time tables
- Linking tables with different granularity levels
- Header and detail tables: Order + Order details, PO + PO Lines etc.
- Effectively using inactive relationships
- Slowly Changing Dimensions

AI/ML + Power BI = Insights ^ 2 en

AI and Machine Learning are very hot topics at the moment. In this session, I'll demonstrate how to leverage these technologies within Power BI for deeper insights. The best part? No need for an advanced degree in Math or experience in Python!

Through numerous demos, we'll delve into:
- Exploratory Data Analysis in Power BI: Understand the correlation between various data columns in your dataset. We'll visualize these with scatter diagrams, histograms, pair plots, correlation matrices, and the Key Influencers Visual.
- Introduction to Machine Learning: Distinguish between Classification vs. Regression Models. We'll also discuss test, validation, training data, model performance, and AutoML.
- Regression Models in Dataflows: Predict values such as revenue, stock, or house prices.
- Classification Models in Dataflows: Determine classes, like identifying prospects most likely to make a purchase.

Forecasting with Python in Power BI nl en

DAX is a powerful language for data analysis in Power BI, but when it comes to forecasting, you quickly run into its limitations. Fortunately, the Python integration in Power BI and Power Query offers a solution.

In this technical deep dive, you'll learn how to build forecasting models using Python.

You will discover:

How to integrate Python into Power Query

The fundamentals of Time Series Forecasting: trend, seasonality, and residuals

The importance of stationarity

Classic models such as Differencing, Autocorrelation, AutoRegression (AR), Moving Average (MA), ARIMA, and SARIMA

Modern tools like Facebook Prophet

Fabric Data Agents & Power BI: de toekomst van self-service BI? en nl

Met de komst van Copilot en Data Agents in Microsoft Fabric wordt je semantische model ineens veel meer dan alleen de basis voor je rapportages. Het wordt het brein van een AI-assistent die zelfstandig inzichten genereert, vragen beantwoordt en zelfs acties onderneemt.

In deze sessie vol demo's ontdek je:
- Wat Fabric Data Agents zijn en hoe ze jouw Power BI semantisch model gebruiken
- Hoe je een model AI-ready maakt met heldere naamgeving, beschrijvingen en relaties
- Best practices om Copilot in Power BI betere antwoorden en inzichten te laten geven
- Voorbeelden van AI-interacties die verder gaan dan visuals en DAX