Ch 1: SQL Database Job Roles

• Database Intro
• OLTP, DWH, OLAP
• DBMS Basics
• Data Engineer Job Roles

Ch 2: Database Intro & Installations

• SQL Server Installations
• Instance & Collations
• SSMS Tool Installation
• Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

• SQL Basics (DDL, DML, etc..)
• Creating Databases, Tables
• Data Inserts (GUI, SQL)
• Basic SELECT Queries

Ch 4: SQL Basics V2 (Commands, Operators)

• DDL: Create, Alter, Drop
• DML: Insert, Update, Delete
• DQL: Select, Fetch
• Add, Truncate Statements
• SQL Operators

Ch 5: Data Types & Variables

• Integer Data Types
• Character, MAX Data Types
• Decimal & Boolean Data Types
• Date and Time Data Types
• SQL_Variant Type

Ch 6: Data Imports From Files

• Data Imports with Excel
• Data Imports with CSV
• Auto Detection of DataTypes
• OLE-DB Connections
• Order By, TOP, OFFSET

Ch 7: Sub Queries

• Basic Sub Queries
• Aggregations
• Combining Queries
• UNION, UNION ALL

Ch 8: Schemas & Batches

• Schemas: Creation, Usage
• Schemas & Table Grouping
• Real-world Banking Database
• 2 Part, 3 Part & 4 Part Naming
• Batch Concept & “Go” Command

Ch 9: Constraints, Keys & RDBMS

• Null, Not Null Constraints
• Unique Key & Check
• Primary Key Constraint
• Foreign Keys, Default
• DB Diagrams & ER Models

Ch 10: Normal Forms & RDBMS

• Normal Forms: 1 NF, 2 NF
• 3 NF, BCNF and 4 NF
• 1:1, 1:M, M:1 Cardinality
• Cascading Keys
• Self Referencing Keys

Ch 11: Joins & Queries

• Joins: Table Comparisons
• Inner Joins & Matching Data
• Outer Joins: LEFT, RIGHT
• Full Outer Joins & Aliases
• Self Joins & Aliases

Ch 12: Views & RLS

• Views: Realtime Usage
• DML, SELECT with Views
• Excel Analytics with Views
• Important System Views

Ch 13: Stored Procedures – 1

• Stored Procedures: Realtime Use
• Parameters Concept with SPs
• Procedures with SELECT
• System Stored Procedures
• Stored Procedures, Tuning

Ch 14: Stored Procedures – 2

• Merge Statement (Upsert)
• Merge with OLTP & DWH
• Matched and Not Matched
• Merge Statement inside SPs
• SP Recompilations

Ch 15: User Defined Functions – 1

• Scalar Functions in Real-world
• Inline & Multiline Functions
• Parameterized Queries
• Variables & Parameters
• Function Executions

Ch 16: User Defined Functions – 2

• Date & Time Functions
• String Functions & Queries
• Aggregated Functions & Usage
• Window Functions (Rank)
• Row_Number, DenseRank
• Partition By & Order By

Ch 17: Triggers & Automations

• Need for Triggers in Real-world
• DDL & DML Triggers
• For / After Triggers
• Instead Of Triggers
• Memory Tables with Triggers
• Disabling DMLs Triggers

Ch 18: Group By Queries

• Group By, Distinct
• GROUP BY, HAVING
• Cube( ) and Rollup( )
• Sub Totals & Grand Totals
• Grouping( ) & Usage

Ch 19: Joins with Group By

• 3 Table, 4 Table Joins
• Join Queries & WHERE
• Join Queries & Group By
• IIF(), CASE Statement
• Query Execution Order

Ch 20: Transactions & ACID

• Auto Commit Transaction
• Explicit Transactions
• COMMIT, ROLLBACK
• Checkpoint & Query Blocking
• READPAST, LOCKHINT

Ch 21: Indexes Basics, Tuning

• Clustered Index, Primary Key
• Non Clustered Index
• Query Optimizer
• Tuning Join Queries
• Tuning Group By Queries

Ch 22: CTEs & Tuning

• Common Table Expression
• CTEs for Data Retrieval
• CTEs for DML Operations
• CTEs for Data Cleansing
• Using CTEs with Row Number

Ch 23: Cursors & Temp Tables

• Cursors & Fetch
• Local Temp Tables
• Global Temp Tables
• SELECT..INTO Statement

Ch 24: SQL Server Architecture

• Network Protocols
• Storage Engine
• Query Processing Engine
• Query Execution Order
• SQL OS Components

Ch 25: Capstone Project (HealthCare Management System) 

• ECommerce Database
• Data Validations
• Query Writing
• Query Tuning
• Excel Analytics

Part 1: Fabric Concepts, DWH & Fabric Data Factory
Ch 1: Fabric Introduction

• Need for Fabric, Big Data
• Fabric Data Engineering Model
• Fabric Components (Items)
• Microsoft Fabric: Advantages
• Cloud Warehouse & AI
• AI with CoPilot
• Azure Versus Fabric DWH

Ch 2: Fabric Account, Workspace

• Need for Fabric Workspace
• Workspace Creation Process
• ETL, Storage, Analytical
• Streaming, Monitoring
• Compute & Separation

Ch 3: Fabric OneLake Architecture

• Intelligent Data Foundation
• Polaris Distributed Engine
• Stateless & Stateful
• Cache, Metadata, Xact & Data
• Fabric Tasks, Inputs & DAG
• State Machine & Statistics
• Hot Spot Recovery

Ch 4: Fabric Warehouse

• Fabric Warehouse Creation
• Fabric Warehouse Features
• Fabric Warehouse Properties
• Fabric Warehouse Limitations
• DWH Internal Operations
• Default Schemas & Objects
• SSMS Connections

Ch 5: Fabric Data Types

• Realtime use of Fabric Houses
• Exact, Approximate Numbers
• Date and Time Data Types
• Fixed & Variable Length
• Binary & String Data Types
• Fabric Type Limitations
• Save As table, Save As View

Ch 6: Fabric Caching

• Fabric Caching Process
• In-memory Cache, Disk Cache
• Cache Types: LRU /MRU
• Cold Cache / Cold Run
• Realtime use of Caching
• Performance Advantages
•  Warehouse Optimizations

Ch 7: Fabric Statistics

• Query Engine Options
• Statistics Types
• Leverage Statistics
• Auto, Manual Statistics
• Update Statistics
• Statistics Consistency
• Statistics Lists & Reports

Ch 8: Time Travel

• Continuous Data Protection
• Data Storage, Retention
• FOR TIMESTAMP AS OF
• Time Travel Scenarios
• Time Travel Implementation
• Time Travel on Queries
• Time Travel Limitations

Ch 9: Zero Copy Cloning

• User Layer, Storage Layer
• Cloning & Parquet Files
• Synapse Data Warehouse
• Data History Retention
• Point In Time , Schema Level
• Zero Copy Cloning Limitations

Ch 10: Fabric Security

• Workspace Security
• Warehouse Security
• Item Security & Roles
• Adding AD Users
• Item Security Limitations
• MFA & Client Security

Ch 11: Fabric Copy Job

• ETL Implementation Options
• Copy Job Item
• Data Loads with Copy Job
• Full Loads
• Testing Copy Jobs

Ch 12: Fabric Copy Job

• Incremental Loads with Copy Job
• Business Key Concept
• DWH: Data Storage
• Testing Initial Loads
• Testing Incremental Loads
• Copy Job Limitations

Ch 13: Fabric Data Factory

• Need for Fabric Data Factory
• ETL Operations in FDF
• Data Sources, Transformations
• Activities and Connections
• Data Destinations (Sinks)
• Creating Pipelines

Ch 14: Fabric Pipelines Design

• Creation Options for Pipelines
• Azure SQL DB Data Loads
• Creating Data Sets
• Copy Command Usage
• Run ID & Monitoring
• Pipeline Creation, Verification

Ch 15: Data Loads with Azure

• Azure Data Lake Storage (ADLS)
• Azure BLOB Containers
• Fabric Data Loads From Azure Files
• Fabric Warehouse with Azure
• Run IDs and Activity
• Compressions & Advantages

Ch 16: ETL Staging

• Staging: Advantages
• Caching & Storing Concept
• Staging Types in Fabric
• Workspace & External
• External Stages in Pipelines
• Pipeline Trigger, Monitor

Ch 17: Fabric Aggr Data Loads

• Aggregation Scenarios
• Creating Views in TSQL
• Using Views in FDF Pipelines
• Using Pipeline Editor
• Data Loads to Warehouse
• Pipeline Verifications

Ch 18: Fabric Incremental Loads – 1

• Upsert (Incremental Loads)
• Business Key Concept
• SCD: Slowly Changing Dimension
• Full Loads Vs Incr Loads
• Testing Incrementing Loads
• Pipeline Execution Tests

Ch 19: Fabric Incremental Loads – 2

• Control Tables
• Watermark Columns
• Lookup Activity
• Stored Procedure Activity
• Parameters & Runs
• Concurrency & Batch Count

Ch 20: OnPrem Gateways

• Need for On-Premise Gateway
• Installing & Configuring
• Authentication, Usage
• On-Premise Connections
• Pipelines for Data Loads
• Warehouse Data Storage
• Data Refresh with Gateways

Ch 21: Data Factory Pipeline Tuning

• Intelligent Throughput
• DOCP & Optimizations
• Staging & In-Memory
• Spark Compute Options
• Concurrent Connections
• ETL Partitions in Real-world

Part 2: Fabric Data Flow, Lake House
Ch 1: Fabric Lakehouse

• Fabric Lakehouse Architecture
• Files and Tables Storage
• Direct Lake & AI
• Creating Lakehouse
• Azure SQL Database Source
• UI: Limitations

Ch 2: Lakehouse File Loads

• Creating Lakehouse
• Incremental Refresh
• Computed Tables
• Reusable Transformations
• Scheduling
• Monitoring
• Best Practices

Ch 3: Power Query Level 1

• Power Query Concept
• Fabric Lake House
• ETL, ELT Process with AI
• Data Combine: Union, Append
• Duplicate / Reference Queries
• Warehouse Data Loads

Ch 4: Power Query Level 2

• Table Transformations
• Group By, Transpose
• Header Row Promotion
• Reverse Rows, Count Rows
• Any Column Transformations
• Data Type, Fill & Pivot

Ch 5: Power Query Level 3

• Text Transformations
• Format, SubString
• Number Transformations
• Date Time Transformations
• Add Column Transformation

Ch 6: Power Query Level 4

• Column From Examples
• Conditional Column
• Index Transformation
• Duplicate Rows, Errors
• Advanced Editor

Ch 7: Power Query Level 5

• ETL Parameters
• Big Data Access
• Static Parameters
• Dynamic Parameters
• List Queries
• M Language Expressions

Ch 8: Stream House, KQL

• Need for Stream House
• Auto creation of KQL
• Manual KQL Databases
• Differences with Warehouse
• Differences with Lakehouse

Ch 9: KQL Query Sets

• KQL Database Extraction
• File Imports – on Premises
• Metadata Edit Options
• Query Analytics
• Exports, Visualizations
• Query Sets Versus Notebooks

Ch 10: Fabric Data Activator

• Need for Alerts, Notifications
• Fabric Data Activator Options
• Alert Conditions, Thresholds
• Email Notifications
• Events & Notifications
• Edit / Enable / Disable

Ch 11: Mirror Database

• Need for Mirror Databases
• Configure Mirror Databases
• Data Replication
• Schema Replication
• Connections & Usage
• Mirror DB Practical uses

Part 3: Python, PySpark, DWH
Ch 1: Fabric Notebooks

• Need for Notebooks
• Fabric Notebook Types
• Creating Environment
• Creating Spark Clusters
• Standard, High Concurrency
• Magic Command
• Freeze Cells

Ch 2: Spark SQL – 1

• Spark SQL Notebooks
• Creating Schemas
• Delta Tables
• Parquet Tables
• Spark Joins
• Data Partitioning
• Union, Views in Spark

Ch 3: Spark SQL – 2

• Math, Sort Functions
• String, Date Time Functions
• Conditional Statements
• Data Recovery & Undo
• Version Number
• Describe Extended

Ch 4: Python Intro & Print

• Python Introduction
• Python Versions
• Python in Spark (PySpark)
• Python Print()
• Single, Multiline Statements

Ch 5: Python Variables

• Defining Variables
• Using Variables
• Printing Variables
• Display Variables
• Variable Types
• Multi Value Variables
• If … Else Statement

Ch 6: Python Operators

• Integer Operators
• String Operators
• Arithmetic Operators
• Assignment Operators
• Comparison Operators
• Formatted Strings
• Indexing Operators
• ELIF, ELSE IF Statements

Ch 7: Python Data Types

• Python Data Types
• Integer / Int Data Types
• Float, String Data Types
• List Data Type
• List Items, Indexes
• Tuple Data Type
• Dictionary Data Type

Ch 8: Python Dataframes

• Pandas Module (Python)
• Dataframes from Lists
• Dataframe from Dict
• Pandas Dataframes
• Dataframe print, display

Ch 9: Python Dataframes Transformations

• Append
• Append with NoIndex
• Merge with ON, KIND
• spark.read.csv()
• spark.read.format()

Ch 10: Medallion Architecture

• Bronze, Gold and Silver
• Raw Data
• Data Preparation (Prepping)
• Temporary Views
• Big Data Analytics

Ch 11: PySpark: Medallion Loads 1

• Data Prep (Silver)
• Filtering DataFrame Records
• Removing Duplicate Records
• Sorting and Limiting Records
• Spark SQL Dataframes
• Gold Layer Implementation
• Testing Aggregated Loads

Ch 12: PySpark: Medallion Loads 2

• Azure SQL DB Connections
• SQL Queries in PySpark
• Data Prep (Silver)
• Filtering Null Values
• Grouping and Aggregating
• Spark SQL Dataframes
• Gold Layer Implementation
• Notebook Utilities

Ch 13: PySpark: SCD

• Slowly Changing Dimension
• Merge Into Statement
• Error Handling
• Merge with OLTP Data Sources

Ch 14: PySpark: Widgets

• Notebook Parameters
• Text Widgets
• Parameters & JSON
• Notebook Schedules, Retry
• Modular Notebook Design
• Notebook Chaining

Ch 15: LakeHouse Architecture Optimizations

• Delta Log
• Delta Versioning
• Partition Strategy
• Small File Problem
• Adaptive Query Execution
• Explain Plan, Spark UI
• mssparkutils

Ch 16: LakeHouse Optimizations

• VACUUM, OPTIMIZE
• ZORDER
• Broadcast Join
• Cache, Persist
• Shuffle Optimization

Ch 17: Semantic Models

• Creating Semantic Model
• Spark SQL: DDL, DML
• Adding Refences, Keys
• Using Model Layouts

Ch 18: Fabric Security

• Workspace Security
• Lakehouse Security
• Notebook Security
• Security Principals
• Authentication Options
• MFA (Multi Factor Authentication)

Realtime Project on ECommerce Platform using Microsoft Fabric
Business Scenario

A multinational e-commerce company wants to centralize sales, customers, products, orders,
inventory, logistics and payment data into Microsoft Fabric.
Goal:
Create a scalable Fabric Data Platform capable of processing millions of records daily.
Skills Gained:

• Fabric Pipelines
• Fabric Notebooks
• Fabric Data Flow
• Semantic Models
• Medallion Architecture (Bronze/Silver/Gold)
• Real-Time Industry Experience
• Fabric APIs (REST, Workspace)

Components For Project (From Resume Perspective):

• Bronze
• Silver
• Gold
• PySpark
• CI/CD
• Deployment
• End to End Integrations
• DP-700 Exam: Complete Guidance

Ch 1: Power BI Intro, Installation

• Power BI & Data Analysis
• Power BI Eco System
• Power BI Design Tools
• PBI Hosting Solutions
• Power BI Installation

Ch 2: Report Design Concepts

• Basic Report Design (PBIX)
• Get Data, Canvas (Design)
• Data View, Data Models
• Data Points, Spotlight
• Focus Mode, PDF Exports

Ch 3: Visual Interactions, PBIT

• Visual Interactions & Edits
• Limitations with Visual Edits
• Creating Power BI Templates
• CSV Exports & PBIT Imports

Ch 4: Grouping, Hierarchies

• Creating Groups: Lists
• Creating Groups: Bins
• List Items & Group Edits
• Bin Size & Bin Count

Ch 5: Slicer & Visual Sync

• Slicer Visual in Power BI
• Slicer: Format Options
• Single Select, Multi Select
• Slicer: Select All On / Off
• Visual Sync with Slicers

Ch 6: Hierarchies & Drill-Down

• Hierarchies: Creation, Use
• Hierarchies: Advantages
• Drill Up, Drill Down
• Conditional Drill Down
• Filtered Drill Down, Table View

Ch 7: Filters & Drill Thru

• Power BI Filters
• Basic, Top & Advanced
• Visual Filters, Page Filters
• Report Level Filters, Clear Filter
• Drill Thru Filters & Usage

Ch 8: Bookmarks, Buttons

• Power BI Bookmarks
• Images: Actions, Bookmarks
• Buttons: Actions, Bookmarks
• Page to Page Navigations
• Score Cards, Master Pages

Ch 9: SQL DB Access & Big Data

• SQL DB Access, Queries
• Storage Modes: Direct Query
• Formatting & Date Time
• Storage Modes in Power BI
• Azure (Big Data) Access & Formatting

Ch 10: Power BI Visualizations

• Charts, Bars, Lines, Area
• TreeMaps & HeatMaps
• Funnel, Card, Multrow Card
• PieCharts & Waterfall
• Scatter Chart, Play Axis
• Infographics, Classifications

Ch 11: Power Query Introduction

• Power Query (Mashup)
• ETL Transformations in PBI
• Table Combine Options
• Merge, Union All Options
• Missing Values, Duplicate Records
• Wrong Data Types, Outliers
• Close, Apply & Visualize

Ch 12: Power Query: Table Transformations

• Table Duplicate, Header Promotion
• Group By Transformation
• Aggregate, Pivot Operation
• Reverse Rows, Count Rows
• Advanced Power Query Mode
• Data Cleaning, Null Handling

Ch 13: Power Query: Column Transformations

• Any Column Transformations
• Data Type Detection, Change
• Rename, Replace, Move
• Fill Up, Fil Down
• Step Edits & Rollbacks

Ch 14: Power Query: Text, Date Transformations

• String / Text Transformations
• Split, Merge, Extract, Format
• Numeric and Date Time
• Add Column & Expressions
• Column From Examples

Ch 15: Power Query: Parameters, Expressions

• Parameters in Power Query
• Static Parameters, Defaults
• Dynamic Dropdowns, Lists
• Linking with Table Queries
• Step Edits, Type Conversions
• API & Web Data Sources

Ch 16: Power BI Cloud & Fabric

• Power BI Cloud, Microsoft Fabric
• Microsoft Fabric Concepts
• Fabric One Lake (DWH, LH, etc.)
• Microsoft Fabric Workspace
• Report Uploads (PBIX)
• Report Edits, Semantic Models

Ch 17: Power BI Cloud Dashboards

• Power BI Dashboards
• Dashboard Creation, Usage
• Pin Visuals, Pin LIVE Pages
• Add Image, Video Tiles
• Q&A & Pin Tiles

Ch 18: Power BI Cloud Operations

• Report Shares, Alerts
• Subscriptions, Exploration
• Downloads & Edits
• Report Cloning in Cloud
• QR Codes, Web Publish
• Lineage & Metrics

Ch 19: Power BI Cloud Gateways

• Data Gateways, Data Refresh
• Install, Configure Gateways
• Data Refresh & Scheduling
• Gateway Optimizations
• Incremental Refresh
• Large Dataset Optimization

Ch 20: Power BI Cloud Apps

• Power BI Apps: Creation
• App Sections & Content
• Audience & App Security
• App Updates, Favorites
• App URL, End User Access

Ch 21: Power BI Report Server

• SQL Server 2025
• Power BI Report Server
• RS Config Tool Options
• Report Database, TempDB
• Web Service & Server URL

Ch 22: Paginated Reports

• Report Builder Tool
• Paginated Report (RDL)
• Report Expressions (RDL)
• Tablix, Chart Wizards
• Fields & Drill-Down
• RDL Report Publish

Ch 23: DAX Concepts (Basics)

• DAX Concepts: Intro & Realtime Need
• DAX Columns: Creation, Use
• DAX Measures: Creation, Use
• DAX Functions: IIF, ISBLANK
• SUM, CALCULATE Functions

Ch 24: DAX Quick Measures

• Quick Measures in Power BI
• Running Totals
• Star Rating Calculations
• DAX Measures in Data View
• DAX in Cloud Reports

Ch 25: Data Modelling, DAX

• Dimensions Tables
• Fact Tables & DAX Measures
• Data Models & DDAX Joins
• Star & Snowflake Schemas
• Many-to-Many Relationships
• Calculation Groups

Ch 26: DAX Joins, Variables

• CALCULATEX & Variables
• COUNT, COUNTA, etc..
• SUM, SUMX, etc..
• SELECTED MEMEBER
• Filter Context, RETURN

Ch 27: DAX Models & Calculations

• VAR, SWITCH, SUMMARIZE
• TREATAS, USERELATIONSHIP
• CROSSFILTER, GENERATE
• RANKX, TOPN, WINDOW
• OFFSET, INDEX

Ch 28: DAX Time Intelligence

• Need for Time Intelligence
• Date Table Generation
• Time Intelligence with DAX
• PARALLELPERIOD, DATE
• CALENDAR, Total Functions
• YTD, QTD, MTD with DAX

Ch 29: DAX – Row Level Security

• RLS: Row Level Security
• Data Modelling & Roles
• Add Cloud Users & KPIs
• CoPilot with DAX

Ch 30: Analytical Reports

• Analytical Report Concepts
• Excel with Power BI Cloud
• SQL, AVRO, JSON Sources
• Analyze in Excel
• Excel Pivot Reports

Ch 31: PL 300 Exam Guidance

• PL 300 Exam Guidance
• Exam Samples
• Exam Scenarios

Project Objective

Design and implement a modern Healthcare Data Platform using Power BI Analytics and Reporting services
to process patient, hospital, clinical, and operational data for reporting, analytics, and decision-making.
Technologies Used

• On-Premise SQL Server
• Azure SQL Database
• Power BI Service
• Power Pivot, CoPilot
• Report Builder

Learning Outcomes
After completing this project, you can confidently showcase experience in:

• Power BI Reporting
• Power BI Service, CoPilot
• Power BI Server
• Azure Synapse with Power BI
• Spark Schemas with Power BI
• Performance Optimization
• End-to-End Azure Data Engineering Solutions

Career Guidance

• ATS-Friendly Resume
• Resume Optimization
• Interview Strategy
• Job Search Platforms