Microsoft launches Fabric, a new integrated platform for data analytics, management and machine learning

It is built on OneLake, a new data lake that is in preview, as of now, and can access data from Amazon S3 as well as Google cloud.

On May 23, Microsoft unveiled Microsoft Fabric, an innovative platform that consolidates its range of data management, analytics, and machine learning tools into one comprehensive solution. This platform is constructed upon OneLake, a cutting-edge data lake currently available for preview, enabling users to seamlessly retrieve data from both Amazon S3 and Google Cloud.

The software can also integrate Azure Data Factory, Azure Synapse Analytics and Power BI into a single unit, particularly to aid business professionals with relevant data.

Arun Ulagaratchagan, Microsoft corporate VP of Azure Data, described Microsoft Fabric as an “end-to-end, unified analytics platform that brings together all the data and analytics tools that organizations need” in a blog post.

One Stop Solution

The platform assists developers in extracting information from a particular set of data and presenting it to the businesses, stated the company’s announcement at Build 2023.

Fabric OneLake

Incorporated into the platform and easily available to every user of Fabric, OneLake has already been introduced by the firm. Through OneLake, the data is automatically indexed as well as structured in a space that is quite user-friendly.

Ready for Business

Microsoft 365 is included in Fabric, a suite that is the base of almost every organisation.

Powered by AI

Microsoft Fabric enables users to help design data flows, generate codes and full-fledged functions, create machine learning functions as well as visualise findings using human-like interactions. This interactive interface also combines models and data from the Azure OpenAI service and publishes them as plug-ins.

Cost Effective

During the event, the firm also spoke about the easy management of resources and purchases. Costs can be optimised through the implementation of a unified resource, as any surplus computing power from one workload can be used by another task.