• Overview of Microsoft Fabric

        What is Microsoft Fabric?

        Key features and components of Microsoft Fabric.

        Comparing Microsoft Fabric with other data platforms like Azure Synapse, Power BI, etc.

        Use cases and business applications of Microsoft Fabric.

• Architecture of Microsoft Fabric

        Core components of Microsoft Fabric: Data Engineering, Data Science, Data Warehousing, Business Intelligence (Power BI), and Machine Learning.

        Overview of Fabric's unified analytics experience.

        Integration of data lakes, data warehouses, and operational data stores in Fabric.

• Getting Started with Microsoft Fabric

        Signing up and setting up a Microsoft Fabric environment.

        Navigating the Microsoft Fabric interface and workspace.

        Understanding the roles and permissions in Microsoft Fabric.

• Data Lake House Concept

        Introduction to Data Lake Houses in Microsoft Fabric.

        Understanding Delta Lake format and its advantages.

• Data Ingestion and Preparation

        Connecting to various data sources (SQL, CSV, Excel, REST APIs, etc.).

        Ingesting structured and unstructured data into Fabric.

        Data cleaning and transformation using Microsoft Fabric’s tools.

• Data Pipelines

        Building data pipelines for data integration.

        Scheduling and orchestrating data workflows.

        Monitoring pipeline performance and handling errors.

• Data Storage

        Understanding storage options within Microsoft Fabric.

        Data partitioning and management best practices.

        Optimizing storage and performance.

• Overview of Data Warehousing

          Introduction to data warehousing and its importance in business intelligence.

          The architecture of a modern data warehouse in Microsoft Fabric.

• Creating and Managing Data Warehouses

          Building and managing data warehouses in Microsoft Fabric

          Data modeling for a data warehouse (star schema, snowflake schema).

          Managing large datasets and performance optimization.

• ETL (Extract, Transform, Load) Process

          Best practices for ETL within Microsoft Fabric.

          Automating ETL pipelines.

          Managing data flow from staging to final datasets.

• Introduction to Data Science Workspaces

          Setting up a data science workspace in Microsoft Fabric.

          Managing and collaborating on data science projects.

• Building Machine Learning Models

          Using Fabric’s integrated tools for building, training, and deploying machine learning models.

          Pre-built AI capabilities in Microsoft Fabric (e.g., text analytics, image recognition).

          End-to-end model lifecycle management (data preprocessing, training, evaluation).

• Model Deployment and Monitoring

          Deploying machine learning models to production.

          Monitoring model performance over time

          Handling model drift and updates.

• Power BI Integration

          How Power BI fits into the Microsoft Fabric ecosystem.

          Connecting Power BI to data stored in Microsoft Fabric for reporting and analysis.

          Creating and publishing Power BI reports from Fabric..

• Building Dashboards and Reportss

          Designing and visualizing data using Power BI within Microsoft Fabric.

          Creating interactive dashboards and visualizations.

          Advanced Power BI features like Q&A, bookmarks, and drill-through.

• Data Governance and Security in Power BI

          Implementing row-level security (RLS) in Power BI reports.

          Managing access to data and reports.

          Compliance and data privacy considerations.

• Building Real-Time Pipelines

          Ingesting and processing streaming data.

          Using Apache Spark, Delta Lake, and other tools for real-time processing.

• Real-Time Dashboards and Alerts

          Visualizing real-time data in Power BI dashboards.

          Setting up real-time alerts based on data thresholds.

• Data Governance Overview

          Importance of data governance in an enterprise environment.

          Key concepts in data governance (metadata management, lineage, data quality).

• Data Security and Compliance

          Managing security for data within Microsoft Fabric.

          Role-based access control (RBAC).

          Compliance with industry regulations (GDPR, CCPA, etc.)..

• Data Lineage and Auditing

          Tracking data lineage in Fabric to understand data flow.

          Auditing data access and transformations for compliance.

• Managing Workspace

          Creating and organizing workspaces within Microsoft Fabric.

          Collaboration and sharing resources with teams.

• Monitoring Pipelines and Workloads

          Setting up monitoring for data pipelines and workflows.

          Performance tuning and troubleshooting issues.

• Cost Management and Optimization

          Understanding pricing models and managing costs in Microsoft Fabric.

          Optimizing resource usage for cost-effective operations.

• Integration with Azure Services

          How Microsoft Fabric integrates with other Azure services like Azure Synapse, Azure Machine Learning, and Azure Data Factory.

• Custom Analytics and Extensions

          Using Python, R, and other scripting languages for custom analytics within Fabric.

          Extending Microsoft Fabric with custom connectors and models.

• AI & Automation with Power Automate and Azure Logic Apps

          Automating tasks in Microsoft Fabric using Power Automate.

          Setting up workflows to automate business processes.

• Nano Commerce ERP Project:

          Apply all the concepts learned to solve a real-world business problem.

• Project Presentation:

          Demonstrating data pipelines.

          Demonstrating data reports.

          Demonstrating ML models.