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Microsoft Fabric Updates Blog

  1. Posodobitve
  2. Marc Bushong

Mastering Enterprise T-SQL ETL/ELT: A Guide with Data Warehouse and Fabric Pipelines

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.

Bridging Fabric Lakehouses: Delta Change Data Feed for Seamless ETL

Within Microsoft Fabric, Delta Tables serve as a common file/table format. These tables find application both in the Data Warehouse and as managed tables within the Lakehouse. Their versatility extends to several features and functionalities, making them indispensable in various use cases. One such feature is the Delta Change Data Feed. While not new to Microsoft Fabric, this … Continue reading “Bridging Fabric Lakehouses: Delta Change Data Feed for Seamless ETL”

Lakehouse vs Data Warehouse vs Real-Time Analytics/KQL Database: Deep Dive into Use Cases, Differences, and Architecture Designs

With the general availability of Microsoft Fabric this past Ignite, there are a lot of questions centered around the functionality of each component but more importantly, what architecture designs and solutions are best for analytics in Fabric. Specifically, how your data estate for analytics data warehousing/reporting will change or differ from existing designs and how to choose the right path moving forward. This article will be focused on helping you understand the differences between the Data Warehouse and Data Lakehouse, Fabric solution designs, warehouse/lakehouse use cases, and to get the best of both Data Warehouse and Data Lakehouse.