This repository documents hands-on exercises completed during the Microsoft Fabric Bootcamp. It covers key concepts such as Lakehouse, Warehouse, Power Query transformations, Data Pipelines, Semantic Model, Dashboard Making, Report Making and practical experience with Dataflow Gen2 within Microsoft Fabric.

• What is Microsoft Fabric?
• Key Features of Microsoft Fabric
• Overview of OneLakehouse architecture
• Microsoft Fabric components and ecosystem
• Use cases and business value

1. Free Mirroring Storage
   

• Understanding the benefits of mirrored data storage for reliability.

1. Who Should Use Microsoft Fabric
   

• Identifying target users and organizations for Fabric adoption.

1. OneLake Overview
   

• Introduction to Microsoft Fabric’s unified data lake.

1. What is Lakehouse
   

• Exploring the hybrid data storage and analytics architecture.

1. What is Warehouse
   

• Explanation of the compute layer used for querying data.

1. Why We Need Domains
   

• Importance of domains for managing data access and governance.

1. Admin Portal Overview
   

• Managing settings and resources through the Fabric admin portal.

1. How to Create a Workspace
   

• Step-by-step process of setting up a workspace in Fabric.

1. Worked with SQL Query Editor using T-SQL

• Querying Lakehouse tables via SQL Analytics Endpoint

1. Used Notebook in Microsoft Fabric

• Running T-SQL queries interactively and viewing results

1. Explored Lakehouse Structure

• Accessed schemas and tables like fact_sale and dimension_stock_item

Notebook Link: click here

Practiced Spark SQL queries in a notebook connected to a Lakehouse.

Notebook Link: click here

Practiced T-SQL Queries in Notebook (Connected to Warehouse)

Notebook Link: click here

This exercise covers loading data from a single Web API source and managing it across storage destinations in Microsoft Fabric, with transformations.

This section documents various data transformation techniques using Power Query in Microsoft Fabric.

• Creating Pipeline using Dataflow Gen2
• Creating and naming a new pipeline
• Adding pipeline activities and configuring Copy Data Assistant
• Connecting to HTTP data source (GitHub raw Excel)
• Configuring Excel file format and previewing data
• Selecting OneLake Lakehouse as destination and defining load settings
• Mapping data types at destination
• Validating and running the pipeline
• Adding Dataflow activity and setting execution order
• Setting up failure notifications via Outlook
• Scheduling pipeline runs for automation

README FILE: click here

• Introduction to notebooks in Microsoft Fabric
• Creating and using notebooks within a Lakehouse
• Loading data from Lakehouse tables into PySpark DataFrames
• Performing transformations and analysis using PySpark
• Demonstrated different types of joins using PySpark:
  • Inner Join
  • Left Join
  • Right Join
  • Full Outer Join
  • Left Anti Join
  • Full join
  • cross join
• Filtering, selecting columns, Dropping columns, and aggregating data using PySpark
• Calculating Gross Sales, COGS, Discount using withColumn
• Saving Dataframe as delta table in pyspark
• Saving a DataFrame as a delta Table in Overwrite Mode

Notebook Link: click here

• Workspace creation in Fabric
• Lakehouse setup with Master and Fact schemas
• Creating table shortcuts from Lakehouse and Warehouse
• Configuring a semantic model using Direct Lake
• Creating and activating table relationships
• Finalizing model for reporting
• Creating Report

Notebook Link: click here

• Working with KQL Queries
• Making Real Time Dashboard

Link: click here