Welcome to the August 2024 Update. Here are a few, select highlights of the many we have for Fabric. V-Order behavior of Fabric Warehouses allows you to manage the V-Order behavior at the warehouse level. Monitor ML Experiments from the Monitor Hub allows you to integrate experiment items into Monitoring Hub with this new feature. … Continue reading “Microsoft Fabric August 2024 Update”
Problem Statement In the previous blog on Profiling Microsoft Fabric Spark Notebooks with Sparklens, we covered how to run Sparklens to profile and tune the performance of your spark notebooks in Microsoft Fabric. In that blog, we used a custom Sparklens JAR. The Sparklens JARs available in the Maven Central repo supports only the Spark … Continue reading “Building a Custom Sparklens JAR for Microsoft Fabric”
We are excited to share guidance for how you can use Microsoft Fabric to turn your data into knowledge for Generative AI applications. This guide will walk you through implementing a RAG (Retrieval Augmented Generation) system in Microsoft Fabric using Azure OpenAI and Azure AI Search. By the end, you’ll be more familiar with how to … Continue reading “Building Custom AI Applications with Microsoft Fabric: Implementing Retrieval Augmented Generation for Enhanced Language Models”
We are excited to announce the Capacity Pools for Data Engineering and Data Science in Microsoft Fabric. As part of the Data Engineering and Science settings in the Admin portal, capacity administrators can create custom pools based on their workload requirements. Optimizing Cloud Spend and Managing Compute Resources In enterprise environments, managing cloud spending and … Continue reading “Introducing Capacity Pools for Data Engineering and Data Science in Microsoft Fabric”
Developing ETLs/ELTs can be a complex process when you add in business logic, large amounts of data, and the high volume of table data that needs to be moved from source to target. This is especially true in analytical workloads involving relational data when there is a need to either fully reload a table or incrementally update a table. Traditionally this is easily completed in a flavor of SQL (or name your favorite relational database). But a question is, how can we execute a mature, dynamic, and scalable ETL/ELT utilizing T-SQL with Microsoft Fabric? The answer is with Fabric Pipelines and Data Warehouse.
