Ch 1: Data Engineer Job Roles

• Introduction to Data
• Data Engineer Job Roles
• Data Engineer Challenges
• Data and Databases Intro

Ch 2: Database Intro & Installations

• Database Types (OLTP, DWH, ..)
• DBMS: Basics
• SQL Server 2025 Installations
• SSMS Tool Installation
• Server Connections, Authentications

Ch 3: SQL Basics V1 (Commands)

• Creating Databases (GUI)
• Creating Tables, Columns (GUI)
• 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, Add, modify, etc..
• DML: Insert, Update, Delete, select into, etc..
• DQL: Fetch, Insert… Select, etc..
• SQL Operations: LIKE, BETWEEN, IN, etc..
• Special Operators

Ch 5: Data Types

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

Ch 6: Excel Data Imports

• Data Imports with Excel
• SQL Native Client
• Order By: Asc, Desc
• Order By with WHERE
• TOP & OFFSET
• UNION, UNION ALL

Ch 7: Schemas & Batches

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

Ch 8: Constraints, Keys & RDBMS – Level 1

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

Ch 9: Normal Forms & RDBMS – Level 2

• Normal Forms: 1 NF, 2 NF
• 3 NF, BCNF and 4 NF
• Adding Keys to Tables
• Cascading Keys
• Self Referencing Keys
• Database Diagrams

Ch 10: Joins & Queries

• Joins: Table Comparisons
• Inner Joins & Matching Data
• Outer Joins: LEFT, RIGHT
• Full Outer Joins & Aliases
• Cross Join & Table Combination
• Joining more than 2 tables

Ch 11: Views & RLS

• Views: Realtime Usage
• Storing SELECT in Views
• DML, SELECT with Views
• RLS: Row Level Security
• WITH CHECK OPTION
• Important System Views

Ch 12: Stored Procedures

• Stored Procedures: Realtime Use
• Parameters Concept with SPs
• Procedures with SELECT
• System Stored Procedures
• Metadata Access with SPs
• SP Recompilations

Ch 13: User Defined Functions

• Using Functions in MSSQL
• Scalar Functions in Real-world
• Inline & Multiline Functions
• Parameterized Queries
• Date & Time Functions
• String Functions & Queries
• Aggregated Functions & Usage

Ch 14: 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 15: Transactions & ACID

• Transaction Concepts in OLTP
• Auto Commit Transaction
• Explicit Transactions
• COMMIT, ROLLBACK
• Checkpoint & Logging
• Lock Hints & Query Blocking
• READPAST, LOCKHINT

Ch 16: CTEs & Tuning

• Common Table Expression
• Creating and Using CTEs
• CTEs, In-Memory Processing
• Using CTEs for DML Operations
• Using CTEs for Tuning
• CTEs: Duplicate Row Deletion

Ch 17: Indexes Basics, Tuning

• Indexes & Tuning
• Clustered Index, Primary Key
• Non Clustered Index & Unique
• Creating Indexes Manually
• Composite Keys, Query Optimizer
• Composite Indexes & Usage

Ch 18: Group By Queries

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

Ch 19: Joins with Group By

• Joins with Group By
• 3 Table, 4 Table Joins
• Join Queries with Aliases
• Join Queries & WHERE, Group By
• Joins with Sub Queries
• Query Execution Order

Ch 20: Sub Queries

• Sub Queries Concept
• Sub Queries & Aggregations
• Joins with Sub Queries
• Sub Queries with Aliases
• Sub Queries, Joins, Where
• Correlated Queries

Ch 21: Cursors & Fetch

• Cursors: Realtime Usage
• Local & Global Cursors
• Scroll & Forward Only Cursors
• Static & Dynamic Cursors
• Fetch, Absolute Cursors

Ch 22: Window Functions, CASE

• IIF Function and Usage
• CASE Statement Usage
• Window Functions (Rank)
• Row_Number( )
• Rank( ), DenseRank( )
• Partition By & Order By

Ch 23: Merge(Upsert) & CASE, IIF

• Merge Statement
• Upsert Operations with Merge
• Matched and Not Matched
• IIF & CASE Statements
• Merge Statement inside SPs
• Merge with OLTP & DWH

Ch 24: Key Take-Aways from Module 1

• Case Study 1: Medicare Scenario
• Case Study 2: Ecommerce Scenario

Ch 1: Fabric Introduction

• Need for Fabric, Big Data
• Fabric Data Engineering Model
• Fabric Components (Items)
• Microsoft Fabric: Advantages
• Cloud Warehouse Uses
• Benefits of Fabric Over Azure
• Azure Versus Fabric DWH

Ch 2: Fabric Account, Workspace

• Need for Fabric Workspace
• Workspace Creation Process
• Pins and New Items
• Item Categorization
• ETL, Storage, Analytical
• Streaming, Monitoring
• Compute & Separation

Ch 3: Fabric 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

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

Ch 6: SSMS Connections

• Warehouse SQL Connection
• Database Engine Server
• Multi Factor Authentication
• Warehouse Artifacts
• Executing .SQL Scripts
• Testing Fabric Artifacts

Ch 7: 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 8: Fabric Statistics

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

Ch 9: 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 10: Aggregated Data Store

• Options for Data Aggregations
• Save As table, Save As View
• Single Table Aggregations
• Multi Table Aggregations
• Dynamic Conditions
• Parameterized Aggregations

Ch 11: 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 12: Fabric Security

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

Ch 13: Fabric Data Factory

• ETL Implementation Options
• Need for Fabric Data Factory
• ETL Operations in FDF
• Data Sources, Transformations
• Data Destinations (Sinks)
• Creating Pipelines

Ch 14: Fabric Pipelines

• Activities and Connections
• Gateways & OnPrem Access
• Data Sets & Activity Sets
• Data Activator & Alerts
• Run ID & Monitoring
• Pipeline Creation, Verification

Ch 15: Fabric Pipelines Design

• Creation Options for Pipelines
• Azure SQL DB Data Loads
• Creating Data Sets
• RRR Transformations
• Copy Command Usage
• Internal Staging (Workspace)

Ch 16: 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 17: ETL Staging

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

Ch 18: OnPrem Gateways

• Need for On_Premi Gateway
• Installing & Configuring
• Authentication, Usage
• OnPremises Connections
• Pipelines for Data Loads
• Warehouse Data Storage
• Data Refresh with Gateways

Ch 19: Fabric Lakehouse

• Need for Fabric Lakehouse
• Files and Tables Storage
• Data Sources: Parquet Files
• Transformation Options
• Direct Lake Concepts
• Lakehouse Consumption
• Lakehouse Real time Use

Ch 20: Lakehouse File Loads

• Creating Lakehouse
• Copy Data Wizard
• Azure SQL Database Source
• File Data Loads in Lakehouse
• Concurrency & Batch Count
• Pipeline Execution Tests
• Pipeline Monitor Check

Ch 21: Aggregated Data Loads

•  Aggregated Data Store
• Plan & Design Aggregations
• Testing Aggregations
• Pipelines for Data Compute
• Data Copy Options
• Pipeline Optimizations
• Data Loads and Verification

Ch 22: MultiTable Loads in LH

• Table Loads Connections
• Data Load in Lakehouse
• Using Copy Data Wizard
• Data Store in Lakehouse
• View Run History, Executions
• SQL End Points & Access
• Lakehouse Schemas

Ch 23: Lakehouse Visual Queries

• Visual Query Interface
• Visual Editor & Tables / Views
• Merge, Remove, Sort Tfns
• Data Preview, Save As Table
• Save As View : Advantages
• Using Schemas, Identifiers
• TDS Packets & Transfer Units

Ch 24: File Explorer

• Installing One Lake Explorer
• Autocreation of Folders
• Workspace Directories
• Warehouse Directories, Logs
• Lakehouse Folders, Files
• Lakehouse Uploads
• Explorer Tool Limitations

Ch 25: Power Query Level 1

• Power Query Concept
• Need for Power Query
• Data Flow Gen 1
• Data Flow Gen 2
• Power Query Items
• Differences with Copy Activity
• ETL, ELT Process

Ch 26: Power Query Level 2

• Data Flow Gen2 Operations
• PQ Online Editor
• Working with Binary Content
• Detailed Data Options
• Data Cleansing Options
• Step Names, Aggregations
• Warehouse Data Loads

Ch 27: Power Query Level 3

• Binding Power Query Steps
• Edit / Delete Steps
• Optimizing Power Query
• ETL & ELT with Power Query
• Advanced Editor
• M Language Expressions
• Duplicate / Reference Queries

Ch 28: Fabric Notebooks

• Need for Notebooks
• Fabric Notebook Types
• Get / Prep / Analyze
• Sessions, Markdown Folding
• Standard, High Concurrency
• Magic Command
• Freeze Cells

Ch 29: Spark SQL Notebooks

• Creating Environment
• Creating Spark Clusters
• Spark Cluster Compute
• SQL Analytics in Notebooks
• Visual Query Vs SQL
• Cell Execution Options
• Magic Command Usage

Ch30: PySpark Notebooks

• Creating / Using Environment
• PySpark Notebook Sessions
• Reading Source Data
• Data Prep & Aggregations
• Data Loads, Analytics
• Cell Execution Options
• Markdown Cells

Ch 31: StreamHouse, KQL

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

Ch 32: KQL Query Sets

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

Ch 33: Fabric Data Activator

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

Ch 34: Model Layouts

• Need for Layouts
• Creating Model Layouts
• Adding Refences, Keys
• Power BI Semantic Models
• Creating Report Items
• Using Power BI Desktop

Ch 1: Power BI Intro, Installation

• Power BI & Data Analysis
• 5 Design Tools, 3 Techniques
• 2 Hosting Solutions
• Power BI with Co-Pilot & AI
• 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 NavigationsScore 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
• Power Query Expressions
• Table Combine Options
• Merge, Union All Options
• Close, Apply & Visualize

Ch 12: Power Query : Table Tfns

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

Ch 13: Power Query: Column Tfn

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

Ch 14: Power Query: Text, Date

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

Ch 15: Power Query: Parameters

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

Ch 16: Power BI Cloud: Publish

• Power BI Cloud Concepts
• Workspace Creation, Usage
• Report Publish Cloud
• Report Edits in Cloud
• Semantic Models & Usage

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 Sources Configurations
• Data Refresh & Scheduling
• Gateway Optimizations

Ch 20: Power BI Cloud Apps

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

Ch 21: Power BI Report Server

• SQL Server 2025 (Mandatory Installations)
• Power BI Report Server
• Report Server Vs Cloud
• Installation, Configuration
• 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
• DAX Cheat Sheet

Ch 24: DAX Quick Measures

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

Ch 25: Data Modelling, DAX

• Dimensions Tables
• Fact Tables & DAX Measures
• Data Models & Relations
• DAX Expressions
• Star & Snowflake Schemas
• DAX Joins & Expressions

Ch 26: DAX Joins, Variables

• CALCULATEX & Variables
• COUNT, COUNTA, etc..
• SUM, SUMX, etc..
• SELECTED MEMEBER
• Filter Context, RETRUN
• Dynamic Report with DAX

Ch 27: 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 28: DAX – Row Level Security

• RLS: Row Level Security
• Data Modelling & Roles
• Verify Roles (Testing)
• Add Cloud Users to Roles
• Dynamic Row Level Security
• Testing RLS in Power BI

Ch 29: Analytical Reports

• Analytical Report Concepts
• Excel Data Analytics
• Excel with Power BI Cloud
• SQL, AVRO, JSON Sources
• Analyse in Excel (Cloud)
• Excel Reports to Cloud

Ch 30: Power BI AI, CoPilot

• AI Components in Power BI
•  CoPilot Practical Uses
• CoPilot with Desktop
•  CoPilot with Cloud
• Need for AI Analytics (Fabric)
• PL 300 Exam (Microsoft Certified Data Analyst) Guidance
• PL 300 Exam Mocks

DP 600 Exam Pattern
Exam Guidance
Exam Samples
Exam Mocks

DP 700 Exam Pattern
Exam Guidance
Exam Samples
Exam Mocks

This course teaches end-to-end Fabric Data Engineering, Fabric Warehouse, Fabric Lakehouse, Pipelines, Power Query, Spark Notebooks, KQL, Data Activator, and Power BI with AI & CoPilot.

Data Analysts, BI Developers, SQL Developers, Power BI professionals, Data Engineers, and freshers who want to learn Microsoft Fabric from scratch. The course starts from basics.

Module 1: MSSQL & TSQL Queries
Module 2: Fabric Data Engineering
Module 3: Power BI with AI & CoPilot
Module 4: DP-600 Exam Guidance
Module 5: DP-700 Exam Guidance

No. Fabric concepts are taught from scratch, including workspaces, items, data engineering models, and Fabric architecture.

Fabric Workspace, Fabric Warehouse, Fabric Lakehouse, Data Factory, Pipelines, Notebooks (SQL/PySpark), KQL Databases, Data Activator, Power Query, Direct Lake, and Model Layouts.

Yes. Intelligent Data Foundation, Polaris engine, stateful/stateless operations, metadata, caching, statistics, DAG, and performance features are covered.

Yes. Warehouse creation, schemas, objects, caching, statistics, retention, limitations, T-SQL usage, and warehouse optimizations are taught.

Yes. Fabric Data Factory, pipelines, activities, datasets, aggregation loads, staging, on-prem gateway, transformations, and direct integrations.

Yes. Creating pipelines, copy activity, dataset creation, RRR transformations, parameterized aggregations, triggers, monitoring, and workspace staging.

Yes. Lakehouse creation, file loads, parquet data, views, visual queries, SQL endpoints, schemas, transformations, concurrency, and real-time Lakehouse analytics.

Yes. Dataflows Gen 1 & Gen 2, cleansing, transformations, aggregation, M language, binding steps, and advanced editor usage.

Yes. Fabric Notebooks, Spark SQL, PySpark clusters, sessions, cell execution, visual queries, markdown, compute options, and real-world analytics.

Yes. StreamHouse, KQL databases, metadata, analytics, exports, visualizations, and differences between Warehouse, Lakehouse, and KQL.

Yes. Configuring triggers, thresholds, event-based alerts, email notifications, and real-time workflows.

Yes. Power BI Desktop, cloud publishing, dashboards, bookmarks, interactions, Power Query, DAX, semantic models, Report Server, and AI + CoPilot features.

Yes. DAX basics, measures, calculated columns, joins, filters, variables, time intelligence, RLS, star/snowflake schema, and dynamic modelling.

Yes. SQL Server 2025 Report Server installation, configuration, RDL reports, expressions, tablix, charts, and paginated reports.

Yes. Exam patterns, guidance, sample questions, and mock tests are part of Module 4 and Module 5.

Live Online Training, Self-Paced Videos, 1-on-1 Mentorship, Real-Time Projects, Resume Preparation, Mock Interviews, DP-600/700 exam support.