We are excited to announce the support of Fabric Git integration and deployment pipelines for data warehouse in Microsoft Fabric! Warehouse in Microsoft Fabric, built on the evolution of SQL server, already offers local CI/CD experiences today. This blog will focus on how you can take advantage of CI/CD capabilities directly in the web, as … Continue reading “CI/CD with Warehouses in Microsoft Fabric”
In the rapidly evolving world of generative artificial intelligence, historical data plays a significant role in influencing the decision-making process and shaping organizational strategies. Data retention within data warehouse refers to the practice of preserving and managing previous iterations of the data encompassing any inserts, updates or deletes made to the warehouse for a specified … Continue reading “Announcing the General availability of time travel and 30 days of data retention in Fabric Warehouse”
In today’s rapidly evolving data management landscape, maintaining the resilience and continuity of your data infrastructure is essential. Unplanned system failures and scheduled maintenance alike demand the ability to restore data warehouses swiftly and seamlessly. This capability is no longer just a feature – it’s a critical necessity in modern analytics environments. A quick and … Continue reading “Seamless Data Recovery through Warehouse Restoration within Fabric Query Editor”
When we talk about Microsoft Fabric workspace collaboration, a common scenario is developers and their teams using a shared workspace environment, which means they have access to “live items”. A change made directly within a workspace would override and affect all other developers or users utilizing that workspace. This is where git becomes increasingly important … Continue reading “Microsoft Fabric Lifecycle Management: Getting started with development in isolation using a Private Workspace”
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.
