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Migrate Tableau workloads to Microsoft Fabric

Quadrant Technologies

Tableau customers need to migrate their workspaces to Microsoft Fabric as part of onboarding. Quadrant supports a smooth transition, including transferring dashboards, reports, and data models

Migrating from Tableau to Microsoft Fabric

Quadrant Manage Services

Migrating from Tableau to Microsoft Fabric involves transferring datasets, visualizations, and workspaces to a comprehensive platform that centralizes data management, real-time analytics, and BI. Fabric enhances scalability, flexibility, and provides advanced tools for better insights and unified data handling.

Fabric Adoption Methodology:

Quadrant Managed Services follows structured methodology to onboard customers onto Microsoft Fabric. It involves the following steps:

  • Assessment
  • Adoption – Follows 4 C’s Framework
    1. Competence
    2. Consultation
    3. Certainty
    4. Customer Satisfaction
  • Acceleration
  • Adherence to Enterprise Scale
  • Aligning to industry-standard solutions with Microsoft Fabric's integrated capabilities.

Tableau to Microsoft Fabric Inventory Tool

This tool allows customers to accelerate the Inventory assessment by using Rest APIs for following:

  • Data Ingestion: Migrates data from various sources into Fabric’s unified data ecosystem.
  • Enhanced Data Governance: Fabric’s Medallion Architecture includes data cleansing, standardization, and a Data Quality and Governance Platform (DQGP), ensuring reliable data for analytics.
  • AI-Powered Context Models: Leverages Fabric’s large language models (LLMs) for automated business context modeling, enabling AI-driven insights.
  • Copilot/GenAI Templatized Reports: Provides quick, adaptable reporting with AI assistance, automating report creation and customization.
  • Real-Time Analytics: Supports continuous real-time analytics with end-to-end alerts for time-sensitive insights.

Key Components of the Inventory Tool:

  1. Data Source Inventory:

    • Data Source Type: Database, file, API, etc.
    • Data Volume: Size of the data source.
    • Data Frequency: How often the data is updated.
    • Data Quality: Data cleanliness and accuracy.
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At a glance

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