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

Week 1: Microsoft Fabric Fundamentals & OneLake
Learning Modules

• Introduction to Microsoft Fabric
• Understand OneLake Architecture
• Lakehouse Fundamentals
• Delta Lake Foundations
• Fabric Capacity & Workspaces

Hands-on Labs

• Create Fabric Trial Workspace
• Create Lakehouse
• Upload CSV files
• Explore OneLake Explorer
• Configure Workspace Roles

Learning Outcome

• Understand Fabric architecture
• Differentiate Lakehouse, Warehouse, Event house

Week 2: Data Ingestion & Data Factory

Learning Modules

• Data Factory in Microsoft Fabric
• Dataflows Gen2
• Incremental Loading
• Data Integration Patterns
• Data validation
• SCD Type 1
• SCD Type 2
• Data Pipelines
• Copy Activities & Pipelines
  → Parameters & Variables
  → Expressions
  → Lookup
  → ForEach
  → If Condition
  → Stored Procedure
  → Notebook Activity
  → Pipeline-to-Pipeline execution
  → Scheduling & Triggers
  → Error Handling
  → Retry mechanisms
  → Logging
  → Incremental Loads
  → Metadata-driven Pipelines

Hands-on Labs

Lab 1: Pipeline Development

• Create Pipeline
• Copy from Azure SQL Database
• Copy from ADLS Gen2
• Schedule execution

Lab 2: Dataflows Gen2

•  Import CSV
• Apply transformations
• Load to Lakehouse

Lab 3: Incremental Processing

• Watermark columns
• Historical + Delta Load

Learning Outcome

• Pipeline orchestration
• Incremental ingestion strategies

Week 3: Spark & Notebook Engineering
Learning Modules

•  Apache Spark Fundamentals
• Spark SQL
• PySpark Transformations
• Delta Tables
• Medallion Architecture
• PySpark
  → DataFrames
  → Schema management
  → Joins
  → Aggregations
  → Window Functions
  → Null handling
  → Deduplication
  → JSON processing
  → Explode
  → UDFs
  → Date/time transformations
• Delta Lake
  → MERGE
  → UPDATE/DELETE
  → Schema evolution
  → Time Travel
  → OPTIMIZE
  → VACUUM
  → Partitioning
• Performance
  → Lazy evaluation
  → Shuffle
  → Partitioning
  → Repartition vs Coalesce
  → Cache/Persist
  → Spark execution plan basics

Labs

• Create Notebook
• Bronze Layer
• Raw ingestion
• Silver Layer
• Cleansing
• Deduplication
• Gold Layer
• Business aggregations

Advanced Labs

• Delta Merge
• Upsert operations
• Performance Optimizations
• Partitioning

Learning Outcome

• PySpark development
• Delta Lake engineering

Week 4: Data Warehouse Engineering
Learning Modules

• Fabric Warehouse
• T-SQL Development
• Dimensional Modeling
• Fact & Dimension Design
• Performance Optimization
• Star Schema
• Surrogate Keys
• Fact table grain
• Conformed Dimensions
• Date Dimension
• Incremental Fact Loading

Labs

• Create Warehouse
• Star Schema
  Build:
• FactSales
• DimCustomer
• DimDate
• DimProduct
• Warehouse Objects
• Views
• Stored Procedures
• Functions

Learning Outcome

• Modern Data Warehousing
• Analytical Modeling

Week 5: Real-Time Analytics & Streaming

Learning Modules

• Eventstream
• Real-Time Analytics
• Eventhouse
• KQL Fundamentals
• Streaming Solutions

Labs

• Eventstream
• Simulated IoT Device
• Ingest streaming data
• Route data to Lakehouse
• Eventhouse
• Create KQL Tables
• Build near real-time reporting

Learning Outcome

• Streaming analytic
• Eventhouse & KQL

Week 6: Enterprise Fabric – Security, Governance, DevOps & Monitoring
Learning Modules

• Security in Fabric
• Governance
• Git Integration
• Deployment Pipelines
• Monitoring & Troubleshooting
• Workspace architecture
• Dev/Test/Prod strategy
• Git branching strategy
• Environment configuration
• Secrets/credentials concepts
• Lineage
• Impact analysis
• Capacity monitoring
• Pipeline monitoring
• Spark monitoring
• Cost optimization

Labs

• Security
• Workspace Roles
• Object Level Security
• Row Level Security
• Git Integration
•  Commit Changes
• Monitoring Hub
• Capacity Metrics
• Refresh Tracking

Learning Outcome

• Enterprise Deployment
• Deployment – Dev → Test → Prod
• Branch Management
• Connect Fabric Workspace

Week 7: Real-Time Project
Project Activities

• Ingest data from Azure SQL, ADLS, CSV & APIs
• Build Fabric Data Factory Pipelines
• Implement Full & Incremental Loads
• Create Bronze, Silver & Gold Layers
• Perform Data Cleansing & Transformations using PySpark
• Build Delta Tables
• Implement MERGE & Upsert Operations
• Handle Duplicate & Missing Data
• Build Fact & Dimension Tables
• Implement Star Schema
• Create Fabric Warehouse
• Automate End-to-End Pipeline Execution
• Implement Error Handling & Logging
• Monitor Pipeline & Spark Jobs
• Optimize Lakehouse & Warehouse Performance

Enterprise Deployment

• Configure Dev, Test & Production Workspaces
• Integrate Fabric with Git
• Branch & Version Management
• Configure Deployment Pipelines
• Deploy Dev → Test → Production
• Configure Workspace & Item-Level Security
• Implement RLS / OLS
• Monitor Fabric Workloads
• Capacity & Performance Optimization

Ch 1: Power BI Intro, Installation

• Power BI & Data Analysis
• Power BI Eco System
• Power BI Design Tools
• Power BI Installation

Ch 2: Report Design Concepts

• Basic Report Design (PBIX)
• Data Points, Spotlight
• Visual Interactions & Edits
• Focus Mode, PDF Exports

Ch 3: Grouping, Hierarchies

• Creating Groups: Lists
• Creating Groups: Bins
• Hierarchies & Drill-Downs
• Drill Up, Conditional DrillDown

Ch 4: 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 5: Filters & Drill Thru

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

Ch 6: Bookmarks, Buttons

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

Ch 7: SQL DB Access & Big Data

• SQL DB Access, Queries
• Storage Modes: Direct Query
• Formatting & Date Time
• Storage Modes in Power BI
• Data Modeling & Formatting

Ch 8: Power BI Visualizations

• Charts, Bars, Lines, Area
• Tree Maps & Axis Items
• Funnel, Card, Mult-Row Card
• Pie Charts & Waterfall
• Scatter Chart, Play Axis
• Infographics, Classifications

Ch 9: Power Query Transformations – 1

• 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 10: Power Query Transformations – 2

• Group By Transformation
• Aggregate, Pivot Operation
• Reverse Rows, Count Rows
• Data Cleaning, Null Handling
• Data Type Detection, Change
• Rename, Replace, Move
• Fill Up, Fil Down

Ch 11: Power Query Transformations – 3

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

Ch 12: Power Query Transformations – 4

• Parameters in Power Query
• Static Parameters, Defaults
• Dynamic Dropdowns, Lists
• Linking with Table Queries
• Step Edits, Type Conversions

Ch 13: Power BI Cloud & Fabric

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

Ch 14: Power BI Cloud Dashboards

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

Ch 15: Power BI Cloud Operations

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

Ch 16: Power BI Cloud Gateways

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

Ch 17: Power BI Cloud Apps

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

Ch 18: Power BI Report Server, RDL

• Power BI Report Server
• RS Config Tool Options
• Report Database, TempDB
• Web Service & Server URL
• Report Builder Tool
• Paginated Report (RDL)
• RDL Report Publish

Ch 19: DAX Concepts & Calculations

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

Ch 20: DAX Quick Measures

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

Ch 21: Data Modelling

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

Ch 22: DAX Joins, Variables

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

Ch 23: DAX Models & Calculations

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

Ch 24: 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 25: DAX – Row Level Security

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

Ch 26: DAX – Analytical Reports

• DAX with Excel
• Analytical Reports
• Virtual Cube Concepts
• Cross Filter Reporting
• Alerts, Data Activator

Ch 27: PL-300 Exam Guidance

• PL 300 Exam Guidance
• Exam Samples
• Exam Scenarios

Realtime Project: Enterprise Healthcare Data & Analytics Platform
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

• SQL Server
• MS Excel
• Parquet
• CSV
• PDF
• Azure

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

• Power BI Reporting
• Power BI Data Analytics
• Business Process Understanding
• Performance Optimization
• End-to-End Power BI Implementation in Cloud

Career Guidance

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