Ch 1: SQL Database Job Roles

• Introduction to Data
• Database Intro, Types
• OLTP, DWH, OLAP
• DBMS Concepts
• Database Job Roles
• Data Engineer Job Roles

Ch 2: Database Intro & Installations

• SQL Server Installations
• Instance Concepts
• Authentication Types
• Authentication Modes
• Collation & File Stream
• SQL Server 2025 Installations
• SSMS Tool Installation
• 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
• DML: Insert, Update, Delete
• DQL: Select, Fetch
• SQL Operators
• Special Operators

Ch 5: Excel Data Imports

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

Ch 6: Schemas & Batches

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

Ch 7: Constraints, Keys & RDBMS

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

Ch 8: Realtime Case Study – 1

• Medicare Database
• Patients, Visits, Meds, etc
• Keys, Constraints
• Relations, Data Validations

Ch 9: 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 10: Views & RLS

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

Ch 11: Stored Procedures

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

Ch 12: 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 13: 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 14: Transactions & ACID

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

Ch 15: Indexes Basics, Tuning

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

Ch 16: CTEs & Tuning

• Common Table Expression
• Creating and Using CTEs
• CTEs, In-Memory Processing
• Using CTEs for DML Operations
• SP Recompilations
• IIF(), CASE Statement

Ch 17: 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 18: Sub Queries

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

Ch 19: Joins with Group By

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

Ch 20: Normal Forms & Self Joins

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

Ch 21: Data Types & Variables

• Integer Data Types
• Character, MAX Data Types
• Decimal & Money Data Types
• Boolean & Binary Data Types
• Date and Time Data Types
• SQL_Variant Type
• Variables in SQL
• Cursor Variable & Fetch

Ch 22: Rank Functions, CTEs

• Window Functions (Rank)
• Row_Number( )
• Rank( ), DenseRank( )
• Partition By & Order By
• Using CTEs with Row Number

Ch 23: Merge (Upsert) with SPs

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

Ch 24: Realtime Case Study – 2

• ECommerce Database
• Entities and ER Diagram
• Data Validations
• Query Writing
• Query Tuning

Part 1: Fundamentals, ADF & Synapse
Ch 1: Azure Fundamentals

• Cloud Introduction
• Azure Concepts
• Cloud Implementations: IaaS, PaaS
• Azure ETL & DWH Concepts
• SaaS & Azure Cloud Concepts
• Azure Resources & Groups
• Storage, ETL, IoT Resources

Ch 2: Azure Deployments, Azure SQL

• Azure SQL Server, SQL DB
• Azure SQL Database (OLTP)
• Azure SQL Pool (DWH)
• Connections from SSMS Tool
• Source Data Configurations

Ch 3: Azure Synapse (DWH)

• Synapse Pool Architecture
• Control Node, Compute Node
• DMS (Data Movement Service)
• Connection Strings
• Pause / Resume SQL Pool
• Scale Up / Scale Down

Ch 4: Azure SQL Pool Operations (DWH)

• Creating Tables with TSQL
• Partitioned Tables
• Distributions
• RoundRobin, Hash, Replicate
• DOP Concept
• Big Data Loads with TSQL
• Important DMFs & DMVs

Ch 5: Azure Storage & ADLS

• Azure Storage Account
• Azure Data Lake Storage
• Azure BLOB Containers
• Blob File Uploads
• Azure Tables
• Access Keys
• SAS Keys

Ch 6: Azure SQL DB Migrations

• On-Premise SQL DB, bacpac
• Azure SQL Deployment
• Azure Storage from SSMS
• Azure SQL DB Migration
• Migration Verifications
• Testing Migrations in SQL

Ch 7: Azure Data Factory (ADF)

• Need for ADF & Pipelines
• Data Orchestration with IR
• Integration Runtime Engine
• Linked Services, Datasets
• Pipelines: Copy Data Activity
• Data Flow Activity with IR

Ch 8: Azure SQL DB Loads

• ADF: Author
• Azure SQL Database Reads
• Azure SQL Pool Writes
• Synapse Analytics with IR
• Pipeline Design, Validation
• Pipeline Runs, Monitoring

Ch 9: Pipeline Settings

• ADF Pipeline Settings
• Staging: Advantages
• Reliable Logging
• Best Effort Logging
• DIU & DOCP with IR
• Compressions, Health Check

Ch 10: File Incremental Loads

• File Incremental Loads
• Storage Account, Data Lake
• Binary Copy, Schema Drift
• Staging Concept in ADF
• Initial, Incremental Loads
• Schema & Data Changes

Ch 11: Table Incremental Loads

• Implement SCD with ADF
• Self Hosted IR: Realtime Use
• On-premise Data: Incr Loads
• Copy Method: Upsert, Keys
• Staging & ADF Optimizations
• Pipeline Runs, Activity IDs

Ch 12: ADF: Data Flow – 1

• Data Flow Concepts
• Data Flow Protypes
• Data Flow Workflow
• Data Flow Transformations
• Spark Clusters
• Optimized Clusters, Preview
• ADF Debug Options

Ch 13: ADF Data Flow – 2

• Creating Data Flow Items
• Using Multiple Sinks
• Conditional Split Transformation
• SELECT Transformation
• Sort, Union Transformations
• Pipelines with Data Flow

Ch 14: ADF Data Flow – 3

• Working with Multiple Tables
• Join Transform, Broadcast
• Row Filters, Column Filters
• Surrogate Keys, Derived Cols
• ETL Loads Dates, Sink Options
• Aggregated Data Loads

Ch 15: ADF Data Flow – 4

• Pivot Transformation
• Group By & Pivot Keys
• Column Pattern, Deduplicate
• Lookup, Cached Lookup
• Tuning Transformations
• Tuning Data Flow, Spark

Ch 16: ADF Data Flow – 5

• Lookup Transformation
• Cache Lookup
• Inline Datasets
• Data Validations
• Lookup Versus Joins

Ch 17: ADF Metrics, Alerts

• Azure Insights
• Azure Metrics for ADF
• Azure Metrics for Synapse
• CPU, Memory Metrics
• Alerts and Notifications
• Action Groups, Tuning Options

Ch 18: ADF with Azure Functions

• Azure Functions
• Function Activity in ADF
• Linked Services
• Pipeline Debug
• ADF Activity Controls

Ch 19: ADF Optimizations

• Synapse SQL Pool Partitions
• ADF Partitions
• Broadcast Options
• Staging, Logging
• DIU, DOCP
• Spar Cluster Optimizations

Ch 20: ADF Parameters, Security

• Linked Service Parameters
• Creating Logins
• Users and ETL Permissions
• Parameterize Logins
• Parameterize Users
• Dynamic Linked Services

Ch 21: SCD & ETL with Control Tables – 1

• ADF Templates in Realtime
• Implementing ADF SCD
• Table Incremental Loads
• Creating Control Tables
• Creating Watermark Columns
• Creating ETL Stored Procedures

Ch 22: SCD & ETL with Control Tables – 2

• ADF Lookup Activity
• Delta Data Expressions
• SP Activity & Parameters
• Control Tables, Watermarks
• Pipeline Parameters, SPs
• Dynamic Data Sets, SCD

Ch 23: Synapse Analytics

• Azure Synapse Analytics
• Synapse Deployments
• Synapse Configurations
• ADLS Containers
• Workspace Server Setup
• Synapse Studio (GUI)

Ch 24: Synapse: Dedicated SQL Pools

• Dedicated SQL Pools
• BLOB Data Imports
• Data Source Creations
• TSQL Queries
• Big Data Analytics

Ch 25: Synapse: Serverless Pools

• Serverless Pools
• Serverless Architecture
• Serverless Vs Dedicated Pools
• BLOB Data Imports
• OPENROWSET Operations
• Big Data Analytics

Ch 26: Synapse: Apache Spark Pools

• Apache Spark Pools
• Spark Cluster Concepts
• Nodes and Executors
• PySpark Notebooks
• Notebook Operations
• BLOB Data Imports
• Big Data Analytics
• Pipeline Integrations

Ch 27: CDC in ADF

• CDC: Change Data Capture
• Using CDC in ADF
• CDC Source Configurations
• Incremental Loads with CDC
• New Rows, Net Changes
• CDC Advntages & Performance

Ch 28: CI CD with GitHub

• Creating Github Account
• GIT: Main, Branches
• Connecting with ADF
• Version Changes
• Builds and Deployments
• CI-CD Integrations

Ch 29: Logic Apps with ADLS

• Logic Apps Integrations
• Events & Scripts
• Triggers & Executions
• Automations
• ADLS with Logic Apps

Part 2: Databricks
Ch 1: Databricks Introduction

• Cloud ETL, DWH
• Cloud Computing
• Databricks Concepts
• Big Data in Cloud

Ch 2: Databricks Architecture

• Unity Catalog, Volume
• Spark Clusters
• Apache Spark and Databricks
• Apache Spark Ecosystem
• Compute Operations
• Hadoop, MapReduce, Apache Spark

Ch 3: Unity Catalog

• Unity Catalog Concepts
• Workspace Objects
• Databricks Notebooks
• Databricks Workspace UI
• Organizing Workspace Objects
• Creating Volumes
• Spark Table Creations
• Spark UI: Limitations

Ch 4: Spark SQL: Basics

• Spark SQL Notebooks
• Creating Catalog
• Creating Schemas
• Creating Tables
• Spark Data Types
• PySpark API: SQL Queries
• Dropping Objects

Ch 5: Spark SQL: Table Types

• Delta Tables
• Managed Tables
• External Tables
• Data Partitioning
• Union, Views in Spark
• External Volumes

Ch 6: Spark SQL: Functions

• Math, Sort Functions
• String, DateTime Functions
• Conditional Statements
• SQL Expressions with expr()
• Volume for our Data Assets
• File Formats, Schema Inference
• Spark SQL Aggregations

Ch 7: Spark SQL: Time Travel

• Time Travel Concepts
• Spark DB: Logical Architecture
• Spark DB: Physical Store
• Data File Store
• Log File Store
• Time Travel
• DESCRIBE, EXTENDED
• HISTORY
• Version Numbers

Ch 8: Python: Introduction, Print

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

Ch 9: Python: Variables

• Python Variables
• Variable Declarations
• Variable Values
• Value Types
• Multi Variable Values
• Common Variable Values
• Realtime use of Variables

Ch 10: Python: Operators

• Need for Operators
• Arithmetic Operators
• Assignment Operators
• Comparison Operators
• Operator Precedence
• Operands in Python

Ch 11: Python: Control Statements

• Python Control Structures
• If … Else Statement
• Short Hand If
• ELIF & ELSE IF Statements
• OR, AND Concepts
• Python Loops

Ch 12: Python: Data Types

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

Ch 13: Python: Modules & Dataframes

• Python Modules
• Pandas
• NumPy
• Dataframe Concepts
• Handling Nulls
• Data Cleansing Concepts
• Pandas Series, arrays
• Indexes, Indexed Lists

Ch 14: PySpark Concepts

• Constructing Dataframes
• Single List Dataframes
• Multi List Dataframes
• Pandas Dataframes
• Contact & Union
• Merge
• Join Options with Dataframes

Ch 15: Medallion Architecture – 1

• Medallion Architecture
• Aggregated Data Loads
• Broze, Silver and Gold
• Temp Views
• Spark Tables (Parquet)
• Work with File Sources

Ch 16: Medallion Architecture – 2

• Medallion Architecture
• Azure SQL DB Connections
• Joining Source Tables
• Dataframes, Temp Views
• Aggregated Data Loads
• Gold Data Consumption

Ch 17: Delta Lake

• Databricks DeltaLake
• Schema Evolution
• Azure SQL DB Connections
• Dataframes, Temp Views
• Delta Table API
• Deleting Records
• Updating Records
• Merging Records
• Old History Retention
• Delta Transaction Log

Ch 18: PySpark: Widgets

• PySpark Parameters
• Text Widgets
• User Parameters
• Manual Executions
• Automations
• UI & JSON For Widgets

Ch 19: Lake Flow Jobs

• Worksflows & CRON
• Job Compute, Running Tasks
• Python Script Tasks
• Parameters into Notebook Tasks
• Parameters into Python Script Tasks
• Concurrent Executions, Dependencies
• Branching Control with the If-Else Task

Ch 20: Pyspark: Auto Loader – 1

• AutoLoader Concept
• Cloudfiles Architecture
• Checkpoint Configurations
• Creating Directories
• Reading Databricks Cloud Sources
• Initial Loads

Ch 21: PySpark: Auto Loader – 2

• Reading Streams with Auto Loader
• Reading a Data Stream
• Manually Cancel your Data Streams
• Writing to a Data Stream
• Schema Evaluation Modes
• Adding New Columns
• Workspace Modules

Ch 22: Lake Flow Declarative Pipelines

• SDP: Spark Declarative Pipelines
• Delta LIVE Tables
• Streaming Data Loads
• Bronze, Silver, Gold Data
• Materialized Views
• Pipeline Clusters
• Databricks CLI
• Data Quality Checks

Ch 23: Databricks Optimizations

• Lazy Evaluation
• Explain Plan
• Caching
• Data Shuffling
• Broadcast Joins
• Partitions
• Data Skipping
• Z Ordering
• Liquid Clustering
• VACUUM
• OPTIMIZE

Ch 24: Security Concepts

• Overview of ACLs
• Adding a New User to Workspace
• Workspace Access Control
• Cluster Access Control
• Groups & LakeBridge
• Access Keys (Tokens)

Ch 25: Version Control & GitHub

• Local Development
• Runtime Compatibility
• Git and GitHub Pre-requisites
• Git and GitHub Basics
• Linking to GitHub & Databricks
• Databricks Git Folders
• Project Code to GitHub
• Adding Modules to the Project Code
• Databricks Job Updates, Runs

Ch 26: Azure Databricks – 1

• Deployment Modes
• Classic Deployment
• Azure Databricks Account
• Azure Databricks Workspace
• Databricks Compute
• UBUNTU OS & VNs
• Photon Acceleration
• Scaling & Tuning

Ch 27: Azure Databricks – 2

• Workspace Operations
• Notebooks & Scripts
• ETL & ELT Process
• Widgets, Workflows
• Jobs & Pipelines
• Open Source Databricks Vs Azure Databricks

Ch 28: Azure Databricks Exam (70-750)

• 70-750 Exam Guidance
• Azure IoT and ASA Jobs
• JSON, PARQUET Formats
• Azure Databricks Exam Pattern
• Exam Q & A, Scenarios

Ch 29: Databricks Data Engineer Associate Exam

• Databricks Data Engineer Associate Exam
• AVRO Formats
• Exam Guidance
• Databricks Exam Pattern
• Exam Q & A, Scenarios

Project Objective: 

Build an end-to-end Azure Data Engineering solution to process, transform, and analyze ecommerce business data from multiple sources.

Technologies Used:

• Azure Data Factory (ADF)
• Azure Data Lake Storage (ADLS Gen2)
• Azure Databricks (PySpark)
• Azure SQL Database
• Azure Blob Storage
• Azure Monitor

Skills Gained:

• Data Ingestion & ETL Development
• Azure Data Factory Pipelines
• Databricks & PySpark Transformations
• Data Lake Architecture
• Medallion Architecture (Bronze/Silver/Gold)
• Real-Time Industry Experience

Ch 1: Power BI Intro, Installation

• Power BI & Data Analysis
• Power BI Design Tools
• Power BI Design Techniques
• Power BI 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, U
•  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
•  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

Realtime Project 2: Healthcare Data Engineering Platform

Project Objective
Design and implement a modern Healthcare Data Platform using Azure Data Engineering
services to process patient, hospital, clinical, and operational data for reporting, analytics,
and decision-making

Technologies Used

• Azure Data Factory (ADF)
• Azure Databricks (PySpark)
• Azure Data Lake Storage (ADLS Gen2)
• Azure SQL Database
• Azure Synapse Analytics
• Azure Key Vault
• Azure Monitor
• Power BI
• Microsoft Fabric (Optional Advanced Layer)

Skills Gained

• Healthcare Data Modeling
• Azure Data Factory Pipelines
• Databricks & PySpark
• Azure Data Lake Architecture
• Data Warehousing Concepts
• Power BI Dashboard Development
• Incremental ETL Processing
• Healthcare Analytics Reporting
• Real-World Project Experience
• End-to-End Azure Data Engineering Project

• Microsoft Fabric Concepts
• Fabric One Lake & ETL
• Fabric Configurations
• Azure Versus Fabric Implementations
• Azure to Fabric Migrations