A first time for everything. I want to learn new spells and want to try fabric and power BI for the first time.
Testing import data to Power BI desktop – With both data import and Direct Query.
Setting the Data source credentials to get the queried data to the Power Bi Service.
Test is working – Now lets wave the wand and build!
Fabric
HACK:
Got help from a team in the same house – HUFFLEPUFF POWER.
We can not get the trial to work in our tenant that we have for ACDC, so i had to create a service principal user in the ACDC tenant – and make it available multitenant. And then use this service principal in fabric in my work tenant to get the data in there.
We want to make a lakehouse with fabric, so after the data is clean, we can use it in Power BI and also share the data with other instances that needs to use the data.
Made a new Lakehouse: WizardData
Made the connection to the ACDC tenant
Cleaned the data:
Did this for all 7 tables.
I could not get compliant with the Power BI for my work tenant. So i decided to use Power BI desktop direct query to get the data from Dataverse and build a dashboard.
Start of dashboard: To be continued.
One last comment – We helped another team with the HACK to get the ACDC data into another tenant. COMMUNITY! – SHARING IS CARING!
Recently updates including two more badges: Crawler and Dataminer.
Api, datasets and code are handy tools to share. But as a PowerBI rooky, it can just as difficult to get a overview and navigate through the magic Power BI provides. Thats why I wanted to share some insight into this area to make it easier for those who comes after us.
We have been struggeling for hours to find a way to embed the PowerBI report as an interactive report in the app. With a free trail not all features are possible, and some workarounds are needed.
After a whole lot of tries and fails as you can see below…
… we managed to put a PowerBI tile in the canvas app, with the wanted dimentions to match our apps design.
After publising your report to PowerBI service the same report can be saved as a tile in a dashboard. Having the whole report in one tile will make the vizualisations interactive with each other – which is more user-friendly.
Embedding a Dashboard tile in the Canvas app was not the biggest issue – making it mobile friendly was a bigger problem. It was not possible to create a dashboard with a mobile display that could be embedded in the app. However, splitting one Raport into 2 Dashboard tiles seemed to be the best way to get the wanted result (visually), even though it was not as interactive as we wanted. But its good enough – as shown below.
Getting the data
Dataminer: We created our own data in SharePoint lists showing the House Cup Points, combined with the other lists. This created the base for exporting this data directly into PowerBI
SharePoint as datasource
Lists: Students, PointSystem, Houses, Professors
In the semantic model we configuredSchedueled refresh (every 6th hour), ensuring that the data updates often enough so the students can keep track of their progress.
The semantic model provided enough data to create the golden report we later embedded in the app.
Crawler: Search and AI in PowerBI report
In PowerBI we included this cool visual where the data is searchable simply by using natural language to ask what the data contains. This is a flexible way for users to access data even though it is not in any of the visuals.
Let us demonstrate:
Helping out
Walking down the hallway – I bumped into Sopranova struggelig with the same thing I just figured out how to do. Lending a hand and providing some guidance we managed to create a custom page with a embedded dashboard tile for their app.
We were very happy about that.
So if anyone else needs a hand in this spesific area we are happy to help:)
Greetings, fellow wizards, witches, and tech enchanters! As proud members of House Slytherin, we at Team PowerPotters are no strangers to thinking big, acting boldly, and weaving a little magic into everything we do. This time, we’re thrilled to present our solution—a perfect blend of resourcefulness, ambition, and innovation—that makes a strong case for not one, not two, but three badges: Hogwarts Enchantment, Dataminer, and Stairway to Heaven.
Here’s how we’ve brought our cunning vision to life, blending the wizarding world with business processes and cutting-edge technology.
🪄 Hogwarts Enchantment: Magic in Every Workflow
They say Slytherins know how to blend strategy with creativity, and our solution proves it. By integrating external Harry Potter APIs, enriched with AI magic, we’ve transformed ordinary data workflows into something straight out of the wizarding world:
Magical Data Sources: Using three Harry Potter APIs, we dynamically fetch spell-related data.
AI-Powered Enrichment: OpenAI serves as the magical mind, enriching spell data with unique descriptions, insightful classifications, and relevant connections.
For example, a spell fetched from the API is expanded with product details and seamlessly integrated into Finance and Operations (FO).
Invisible Charms at Play: A clever “dirty hack” with Dataverse acts like an invisible charm to detect duplicates, ensuring a seamless user experience and efficiency.
✨ Why This Deserves the Badge: By blending AI, external APIs, and intuitive workflows, we’ve brought the enchantment of Hogwarts to life within business processes. It’s not just automation—it’s pure magic.
🐍 Stairway to Heaven: Ambition Meets Integration
As true Slytherins, we always aim for the top. With this solution, we’ve ascended the metaphorical stairway to technical greatness by integrating three major Microsoft services into one seamless, end-to-end workflow:
Dataverse: Acting as a logical trap, it checks for duplicates using error-driven logic, saving time and resources.
Finance and Operations (FO): The heart of our solution, FO handles enriched product creation, BOMs (Bill of Materials), and inventory management.
OpenAI: Enhances data with contextual business insights, transforming raw API information into actionable intelligence.
✨ Seamless Integration in Action:
Data flows from the Harry Potter APIs to Dataverse, where duplicate checks occur.
OpenAI adds contextual magic before FO processes the enriched records, ensuring they deliver maximum business value.
✨ Why This Deserves the Badge: The solution builds a stairway that bridges data, AI, and business processes. It’s efficient, scalable, and a testament to the power of intelligent integration.
🧙♂️ Dataminer: Uncovering Magical Insights
Our solution didn’t just extract data—it transformed it into gold worthy of a Slytherin treasure vault:
Using Harry Potter APIs, we mined spell-related data and applied AI to add context, relevance, and business-ready value.
We didn’t stop at fetching data. We turned it into actionable insights that integrate seamlessly into business systems, eliminating manual work and reducing errors.
✨ Why This Deserves the Badge: This isn’t mere data extraction—it’s intelligent, enriched, and purposeful mining that creates real value.
🪄 Why We Deserve All Three Badges
Our solution is a shining example of Slytherin ingenuity, ambition, and teamwork. It seamlessly meets the criteria for:
Hogwarts Enchantment: By combining APIs, AI, and intuitive workflows, we’ve made business processes feel truly magical.
Stairway to Heaven: Through integration of Dataverse, FO, and OpenAI, we’ve built a harmonious system that transforms data into value.
Dataminer: We didn’t just gather data; we mined and enriched it with AI to deliver insights that matter.
🔮 A Slytherin’s Call to Action
House Slytherin has always been about turning ambition into achievement, and this solution is no exception. With our resourceful approach to combining APIs, AI, and business logic, we’ve created a system that not only solves problems but does so with a touch of magic.
We humbly present our work for consideration for the Hogwarts Enchantment, Stairway to Heaven, and Dataminer badges. Ambition, strategy, and ingenuity—it’s the Slytherin way.
(c) Faruk The Fabricator inspired by the Silicon Valley series.
If you think a student’s story begins when they enroll at Hogwarts, you could not be more wrong. The Fabricator is evil and does not care about privacy. The Fabricator is guileful and does not care about truth. He will do everything in his power to gather or fabricate every detail of their lives and use it to achieve his goals.
At the moment, The Fabricator uses Fabric to access previous data of the students wishing to enroll at Hogwarts. We call the Kaggle API within notebook code to retrieve data from Kaggle and write it as a CSV file.
Python code in another notebook is then used to transform this data and divide it into clusters.
Finally, a “Copy Data” activity moves the data to its final destination. But is this truly the end?
Follow the Fabricator for more—if you can, that is.
In the coming days, the Fabricator plans to:
Show clustered data in Power BI reports.
Use insights to plan interventions or recommendations for students.
Perform behavioral predictions: Use the clusters as labels for supervised learning models to predict future performance.
Trigger emails or alerts for specific clusters needing attention.
Data is born into Fabric, molded by it. Data does not see the light until it is ready to face users. And when it is finally presented, it is blinding.
(c) The Fabricator and The Batman.
PS: with this article we claim the following badges:
Thieving Bastards – we use online data source from kaggle
Dataminer – we are doing data transformation for better reporting and we are using extrernal data.
Go With The Flow – we create the pipeline that can be used to retrive any data from kaggle. We plan to use data activators to send alerts based on the processed data.
Power User Love – in fabric we created pipeline as a low code solution. inside pipeline we are using python code for advanced operations.
Nasty Hacker – for retrieving data from 3rd party services and syncing it to the blob storage to connect to Azure AI Studio
Data miner – for retrieving the case information from dataverse and calculating the average score using AI.
Embedding numbnut – for embedded copilot in Model-driven app
Stairway to heaven – for using Azure AI Studio, Copilot, Blob storage, and in previous articles also Azure Function, WebApp
Our solution includes the latest features of the PowerPlatform and Azure connection the Low and Pro code approaches together, to allows you to boost the performance of resolving the cases by using some insights from the Copilot.
The business use case is about complicated cases when we need external consultancy and assistant, so task is to find the suitable Marios-consultants according to the customer request, by searching the professionals on the Indeed, comparing their background and experience and finding the best matches. (to find the suitable Marios according to the request.)
The business use case is about simplify the KYC(Know Your Customer) process by using unified workspace for all operations. From Indeed we can understand the company background,do semantic analysis of the comments to have insights on how technicians can approach customer (princess) (simplify the KYC(Know Your Customer) process by using unified workspace for all operations.)
The business use case is about analyzing current princess (customer) and her needs based on the Indeed. The data that we were able to retrieve contains open job postings, company in for, recent news and personal profile info. Based on this information, we can suggest more other services and provide information to sales and marketing departments. (analyze current princess and her needs based on the Indeed profile and suggest more other services.)
We deployed GPT-4 model to our personal instance using AI Studio, then fine-tuned it with company’s internal data and data from open sources like Indeed, Glassdor or proff.no.
With RAG, the external data used to augment your prompts can come from multiple data sources, such as a document repositories, databases, or APIs. The first step is to convert your documents and any user queries into a compatible format to perform relevancy search. To make the formats compatible, a document collection, or knowledge library, and user-submitted queries are converted to numerical representations using embedding language models. Embedding is the process by which text is given numerical representation in a vector space.
AI Studio has seamless integrated feature use Azure Blob Indexer to implement search functionality search for AI. It bring the possibility to simplify the access to the Datalake from LLM side.
By implementing multiple connectors to the third-party services and data sources, together with Dynamic chaining feature of the Copilot it gives cleaner user experience for the user using only one tool for analyses.
Dataminer – for using dataverse as a datasource in Power BI, with data from the games.
Plug N’ Play – for embedding the Power BI report into teams.
The data from the Power BI report is coming from a table in dataverse called “Leaderboards”. You have the score of each player for a certain game, and what number of try there was for that game. We then use this data in the Power BI report to show all of the players score in a dashboard. There is also the possibility of seeing one specific players data. Which is of course presented in the same graphical theme as the other apps.
And of course it doesn’t stop there. We then also made sure to embed the Power BI report into Teams. Into our Peaches-team where we can then keep track of the players progress. The report addresses a crucial business need by allowing us to efficiently track and analyze the progress of players in the various games. We can then make data-driven decisions, like seeing which game the kids find more fun and entertaining. This can then be used to further develop games for the kids, and enhance their learning experience. Which will allow us to constantly evolve and improve.
To make our amazing service Tubi work, a lot of cloud is needed. We aim to make the plumber’s job easier by recommending the best layout for where the pipes should go, and for that, we need AI. We have trained a model in Custom Vision to recognize all components in a bathroom that need water. So, when the plumber uploads a floor plan to our Static Web App, the image is sent to our Azure Function App backend in C# Asp.net through our own API. But both the image and the equipment list must be stored somewhere. Therefore, we have also connected to Azure Blob Storage. Then last but not at all least. The people working in the back office have instant interactive reports available to help them with filing and billing through Power BI and alerting the using an automated flow (Badges: Feature Bombing)
Sometimes it works, and that’s plenty
Databases are good, but sometimes it’s easier to just dump everything in one place until you need it again. Yes, it might not be very scalable or very normalized. SQL became too heavy, and we already needed a Blob storage to store the images, so we also dump the order data in the same blob storage as JSON files. It’s old fashioned way of serverstorage, and a bit dirty, but it works! (Badges: Nasty hacker, Retro badge)
Power the backoffice
As the final list of components are decided, they still have to be approved from the accounting team in the office. To make sure they have all the information they require, we have developed a Power BI dashboard to crawl through our registered data and make sure the orders are handled properly (Badges: Crawler, Dash it Out, Dataminer). And to make sure the orders are handled easy and fast, the dashboard is embedded into teams and an alert is automated by using a logic app to make sure the workers can receive and cooperate in realtime (Badges: Embedding Numbnuts, Go with the flow, Pug N’ Play, Power user love, Right Now, Stairway to heaven).
This post has the topic within Most Extreme Business Value category
Dataminer
Dash it out
At first glance, Peach Mini Games might seem like pure fun – a temporary escape from the realities of everyday life and an invitation to indulge into a dream. However, that’s where one truly misses the mark. When deeply exploring Peach Mini Games it is like stepping into a classic ‘Mullet’ situation: Business in the front, party in the back. Or maybe it is the other way around. Anyway…
You’re met with vibrant colors, pixel-perfection that would make anyone envious, and scalability smoother than a cat’s movement. But beneath this lies a complex tapestry of business value.
Hangman Through our Hangman game, one not only learns the player meanings of words but also how they are spelled. Research has shown that the use of Hangman for these purposes is highly effective. Studies indicate that this method is motivational for learning, providing a playful approach that makes it more engaging for participants. Additionally, research suggests that interactivity, such as actively participating in game-based learning like Hangman, can enhance vocabulary and spelling skills more effectively than traditional methods.
Quiz To complete the task of saving Mario, Princess Peach must pass a quiz. The quiz is integrated with ChatGPT and serves both as an entertaining and educational part of the game. At the same time, the concept is so versatile that it can be directly applied to create business value in the field of education.
Imagine a scenario where one configures a quiz within a specific subject area. Students can use this quiz to practice and identify knowledge gaps, while ensuring they rely on reliable sources. This setup provides teachers with control over information access, limiting students’ searches to a secure environment instead of allowing unrestricted internet browsing.
Statistics Similar to how the games described above can motivate learning, our PowerBI scoreboard can better equip teachers to understand what students know and what they need to work on, both on a group and individual level. The statistics can pinpoint specific areas that require more attention, ensuring teachers use their time effectively and work in a more data-driven manner.
In order to train our AI-model we need a lot data, but in order to have the correct data, we also need bananas. Being IT people, we never thought of using actual bananas, but instead had a hotel employee in a suit deliver 20 a3 pages of printed bananas that we cut out and glued to pieces of cardboard. Compete with their individual banana stands:
We then placed the bananas in the road around the hotel, got in the car and drove around filming the road from the dashboard inside the car. The conditions were quite foggy and bad, but the enthusiasm was electric. We ended up with a lot of footage including some of a consenting dog and owner.
We then started feeding all the videos to our model, mining for frames that contained actual bananas – our gold.
This is done in a jupyter notebook in python. The first thing the model does is identify all frames that contained potenital bananas, and singling them out. We ended up with over 10 000 potential banana frames. This is one of them with an obvious banana:
The model then strips away everything that is not a banana, in order to get the precise location of the banana:
Furthermore the model does a cutout of the frame around the banana which ends up being used in our actual dataset giving the banana detection app an excellent base and understanding of how to identify bananas.
Actual video from the training data:
Video of banana making:
Photo gallery of Herman and Erik placing bananas and driving around filming the bananas.
For our solution we heavily rely on 3.rd party services from AI to deliver a unique gaming experience. The first step in our application is asking questions to the user about the experience they want to play.
The values are sent over to a flow that has a custom connection to OpenAI prompting. Based on the promts this provides we will generate a unique gaming experience to our customers.
The first and most important promt is “Creating the Landskape”
The JSON that is received above is a complex structure of X and Y cordinate values. Each one representing a brick or movable space for the game.
Next step is evaluating this data in Power Apps, and the result is a playable game like the one below:
We have several more promts (Boxes in RED) that make up other elements of the game. This combines the API’s of DALL-E and OpenAI Chat promting. Together with these external API’s we generate a unique experience for the gamer each time they play. This value makes us one of the few games out there you can play time and time again without ever having to replay the same track!
