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Microsoft Fabric Updates Blog

  1. Oppdateringer
  2. Data Factory

Easily Move Your Data Across Workspaces Using Modern Get Data of Fabric Data Pipeline

We are excited to share that the new modern get data experience of data pipeline now supports copying to Lakehouse and Datawarehouse across different workspaces with an extremely intuitive experience. When you are building a medallion architecture, you can easily leverage Data Pipeline to copy your data into Bronze Lakehouse/Warehouse across different workspaces. This feature … Continue reading “Easily Move Your Data Across Workspaces Using Modern Get Data of Fabric Data Pipeline”

Microsoft Fabric Lifecycle Management: Getting started with development in isolation using a Private Workspace

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”

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.

Demystifying Data Ingestion in Fabric: Fundamental Components for Ingesting Data into a Fabric Lakehouse using Fabric Data Pipelines

  • Headshot of article author juni 18, 2024 av RK Iyer 112 278 Visninger

✎ Co-Author – Abhishek Narain Overview Building an effective Lakehouse starts with establishing a robust ingestion layer. Ingestion refers to the process of collecting, importing, and processing raw data from various sources into the data lake. Data ingestion is fundamental to the success of a data lake as it enables the consolidation, exploration, and processing … Continue reading “Demystifying Data Ingestion in Fabric: Fundamental Components for Ingesting Data into a Fabric Lakehouse using Fabric Data Pipelines”

A Data Factory Pipeline Navigator Map to a Successful POC

Authors: Prashant Atri & Prabhjot Kaur Welcome to Version 1 of the ultimate Data Factory Pipeline Mind Map! This one-page view is packed with guidelines to help you navigate Data Factory pipelines on your Data Factory journey to build a successful Data Integration project. Starting from the left-hand side, you will find broad feature areas … Continue reading “A Data Factory Pipeline Navigator Map to a Successful POC”