Data processing in today's companies is marked by heterogeneous data storage (SQL, NoSQL, unstructured data, etc.) and processing components (databases, Big Data processors, etc.). Data in a company often passes through complex paths from generation or receipt of the data, through various data processing components, to storage or distribution of the data to various recipients. With Data Factory, local data such as that from SQL Server can be processed together with cloud-related data from Azure SQL Database, Blobs, and Tables. These data processing streams can be created, processed, and monitored through simple, highly available data pipelines. Data sources and data recipients can be defined, and the movement of the data in the company can be traced and monitored from a central location.Pricing SLAs Limits Learning Twitter
The data landscape is more varied than ever with unstructured and structured data originating from many cloud and on-premises sources. Data Factory enables you to process...
Exploring data orchestration concepts? Check out this course on the basic capabilities of Azure Data Factory (ADF). Get an overview of advanced analytics, and see how Azure...
If you’d like to learn how to architect solutions in Cortana Intelligence Suite and how to build intelligence into your applications, don’t miss this workshop! Build an...
This video takes a deeper dive into the features and functions of the Azure Data Factory orchestration engineLearn more: http://aka.ms/g7hlpt
This video takes a deeper dive into how Azure Data Factory can be used to build hybrid big data analytics pipelines through the lens of an automated risk processing use case...