Fabric MVP US

Intro to Microsoft Fabric

Microsoft Fabric represents a cutting-edge, all-in-one analytics solution designed for enterprises, seamlessly integrating data management, data science, real-time analytics, and business intelligence within a unified platform. This SaaS-based offering encompasses data lake management, data engineering, and data integration, all supported by advanced AI capabilities, to provide a comprehensive and integrated solution for your data needs. It simplifies and centralizes the data analytics process, enabling both technical and non-technical users to harness its power, making insights accessible and actionable.

Benefits of Using Microsoft Fabric

Scalability - Designed to be scalable, easily adapting to meet the changing needs of businesses, helping to save costs and improve efficiency.

Simplified Data Management - A unified platform for data management can simplify data storage, organization, and access, improving data quality and reducing time to find and use data.

Enhanced Collaboration - Users can share data and insights, and work together to build and deploy data-driven solutions, making it easy for teams to collaborate on data projects.

Improved Security - Secure environment for data storage and processing, including features such as data encryption, access control, and auditing, helping to protect data from unauthorized access.

Reduced Complexity - Provides a single pane of glass for managing data, including pre-built tools and templates to help automate tasks, reducing the complexity of data management.

Cost Efficiency - A cost-effective solution for data management, helping to save costs by reducing the need for third-party tools and services.

Our Approach

In 3 weeks, our Solution Architects will enable you to quickly understand the features and capabilities of Microsoft Fabric, as well as relevant use-cases that can be delivered by the new solution. 

Timeline

• Week 1
  • Defining and aligning use case to your core business value and key KPI’s
  • Requirements gathering
  • Infrastructure analysis and wireframing
• Week 2
  • Building out the Lakehouse and Power BI premium data model
  • Visualizing data aligning to your branding
  • Producing a deployment plan
• Week 3
  • Deploy and socialize end output to users
  • Capture and incorporate all relevant feedback 
  • Provide roadmap for future iterations