• Exploration Experience: Saving and Creating Datasets
    I have been talking about Data Exploration in Power BI on many of my sessions, specially the sessions about Data Marts. The new data exploration feature is one more feature on this expanding scenario for data exploration. This one brings some interesting details. We start using this feature from a query. The feature will allow […]
  • Fabric and Azure SQL in a few clicks: Mirroring a Database
    Finally, mirroring is available for Fabric! You can mirror an Azure SQL to Fabric. It works for CosmoDB and Snowflake as well, but in this article, I will focus on Azure SQL. It is 100%, no, but it is definitely a feature that is really great even now. Before getting into a step-by-step of the […]
  • Azure OpenAI and RAG Security
    The most watched videos on my youtube channel are related to security. Azure SQL Networking Secrets and Cloud Security: Secure Access from your Applications to Azure SQL explain private endpoints from different points of views: Azure SQL and Function Apps. The concepts of Private Endpoints apply to most Azure features and even the relation of […]
  • Dynamic Partitioning and a Simple Incremental Load
    Let’s consider a simple statement for partitioning and save a table in a lakehouse: df.write.mode("overwrite").format("delta").partitionBy("Year","Month","Day").save("Tables/" + table_name) Let’s consider we load the data daily, with all the transactions from the day. The table will save the transactions for each day in different partitions. We can expect the table to keep the partitions from previous day, […]
  • Creating a Single JSON File for Configuration using Notebooks
    On the blog Fabric Notebook and Deployment Pipelines I explained a technique to keep notebooks configuration values in JSON files on lakehouses, a good solution from many different points of views. What if we need to provide maintenance to the JSON configuration file using notebooks? The first problem is the fact the typical statement to […]
  • Model Optimization: Available In MDX Property
    Power BI Semantic Models have a property called Available In MDX. Each field in the model has this property and we can only see or change its value through XMLA. For example, we can use Tabular Editor for this. We can disable this property in the fields where it will not be needed (and in […]
  • 5 Secrets about Dataflows Gen 2
    Dataflows Gen 2 are the new version of Power BI dataflows. There are so many changes in relation to the previous version they are considered a new feature. The main difference is the possibility to set a target for the result of each query in the dataflow. In this way, it can be used as […]
  • Reverse Creating the PBIP file from Source Control
    On my article about Source Control with GIT, Power BI and Microsoft Fabric, I illustrate how to use the PBIP file format to include Power BI reports and semantic models in a source control process and stablish a SDLC (Software Development Lifecycle) for Power BI. However, the complete explanation is based on saving the development using […]
  • Customizing Kusto (Data Explorer) Connections in Deployment pipeline
    When organizing our SDLC (Software Development Lifecycle) in Power BI/Fabric, we use Deployment Pipelines and create rules to change connection configurations every time we promote an object from one environment (dev for example) to another (test, for example). Kusto connections, on the other hand, are not so simple. You can check more about Deployment Pipelines […]
  • Model Optimization: Available In MDX Property
    Power BI Semantic Models have a property called Available In MDX. Each field in the model has this property and we can only see or change its value through XMLA. For example, we can use Tabular Editor for this. We can disable this property in the fields where it will not be needed (and in […]