The art of simplicity

Loading data into a Microsoft Fabric Data Warehouse can feel effortless—until it suddenly isn’t. Being a total MS Fabric noob, the following error message caused some confusing and brough me on a learning journey I like to share: String or binary data would be truncated while reading column of type 'VARCHAR(255)'. Turns out that if you’re working with Parquet files, multilingual data, or Copy Into pipelines, this error can appear even when the source column seems to match the target column. In this post, I’ll walk through why this happens, why collation changes alone don’t fix it, and what you can do to avoid it. The scenario I was loading data from an on premise datasource through a data gateway into a Fabric Data Warehouse table. The target column is defined as: NAME_LB VARCHAR(255) To already give you a hint, the source column contains values like: RÖ The column lengths match. I even switched the collation to a UTF‑8 compatible one: COLLATE Latin1_General_...

When trying to connect to an Azure Data Lake Storage Gen 2 using PowerBI, it failed with the following error message: "Access to the resource is forbidden" . The first thing I tried was resetting my credentials(just in case). Therefore I went to File –> Options and settings –> Data source settings. There I clicked on Clear permissions . Unfortunately that didn’t help. Although I could access the data lake data directly in the Azure Portal, it turned out that I didn’t had enough rights to access the resource through PowerBI. To fix it I had to go to the Azure Portal and add any of the following rights: Blob Data Reader , Blob Data Contributor , or Blob Data Owner .

In our Azure Data Lake Storage we have data stored in parquet files. Reading this data in PowerBI is not that hard. In this post I'll walk you through the steps to get this done. Start by opening PowerBI and click on Get data from another source . Choose Azure Data Lake Storage Gen2 from the list of available sources and click on Connect . Enter the url of your Azure Data Lake Storage and click on OK. Now you get a list of available files found in the data lake. We don’t want to use these files directly but transform them, so click on Transform Data . This will open up the Power Query editor .  Click here on Binary next to the parquet file we want to extract. This will add an extra step to our Power Query that parses the parquet file and extracts all the data. Click on Close & Apply to apply the changes to our query and start using the results. That’s it! More information Azure Data Lake Storage Gen2 - Power Query | Microsoft Learn Analyze data in Az...

For an Azure Fabric based data platform we are building I'm evaluating PowerBI as a possible reporting solution. My knowledge of PowerBI is almost non existant so there are a lot of things I learned along the way. Yesterday I talked about the different data fetching strategies. Today I want to focus on how to limit the amount of data. I explained yesterday that PowerBI can handle large amounts of data. This is good news but during development I want to work with a smaller dataset to limit the file size and gain some extra development speed. Limit the amount of data Let’s find out how we can get limit the amount of data in PowerBI. Start by opening the Query Editor view in PowerBI by selecting Transform data from the Home tab: This will load the Query Editor . Here we need to create a new parameter that can be used as a limit value. Click on Manage Parameters and choose New Parameter : We create a LimitRows parameter with a default value of 10: Now that we have...

For an Azure Fabric based data platform we are building I'm evaluating PowerBI as a possible reporting solution. My knowledge of PowerBI is almost non existant so there are a lot of things I learned along the way. My first lesson learned: There are multiple ways to fetch data in PowerBI. I learned this lesson quite fast when I tried to connect to my first datasource. After selecting the ‘Import data from database’ option I immediately got  to choose a Data Connectivity mode : The available options were: Import DirectQuery Let’s find out what each of these options mean and what exactly is the difference. Import mode This mode should be your first choice. As the name implies in this mode the data is imported in the report. When querying the data everything is loaded into memory which gives really fast query performance. This sounds a good idea for really small datasets but what if you have a lot of data? PowerBI makes it possible to work with big dataset...