Ch 1: SQL Database Job Roles

• Database Intro
• OLTP, DWH, OLAP
• DBMS Basics
• ETL & 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

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

Ch 7: Schemas & Batches

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

Ch 8: Constraints, Keys & RDBMS

• Unique Key & Check
• Primary Key Constraint
• Foreign Keys, Default
• DB Diagrams & ER Models

Ch 9: 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 10: Joins & Queries

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

Ch 11: Views & RLS

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

Ch 12: Stored Procedures – 1

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

Ch 13: Stored Procedures – 2

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

Ch 14: User Defined Functions – 1

• Functions in Real-world
• Scalar & Table Value Functions
• Parameterized Queries
• Variables & Parameters
• Function Executions

Ch 15: User Defined Functions – 2

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

Ch 16: Triggers & Automations

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

Ch 17: Transactions & ACID

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

Ch 18: Indexes Basics, Tuning

• Clustered Index, Primary Key
• Non Clustered Index
• Query Optimizer
• Tuning Queries

Ch 19: CTEs & Tuning

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

Ch 20: Cursors & Temp Tables

• Cursors & Fetch
• Scroll, Absolute Types
• Local & Global Temp Tables
• SELECT..INTO Statement

Ch 21: Sub Queries

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

Ch 22: Group By Queries

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

Ch 23: Joins with Group By, Sub Queries

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

Ch 24: Linked Servers

• SQL Server Instances
• Linked Server: Creation
• Linked Server: Testing
• Scripting Linked Servers
• Realtime Usage
• Remote DB Access

Ch 25: Server Architecture

• Database Engine Components
• Parser, Compiler & Optimizer
• Protocols and Query Processing
• MDAC and CLR Components
• Parsing and Compilation
• Memory Manager & IO Managers
• SQL OS Components, MDAC

Ch 26: DB Architecture (VLDB)

• Planning Large Databases
• Primary, Secondary Data Files
• Filegroups, Spacing and Sizing
• Log File: Usage and Precautions
• Creating Tables with Filegroups
• Pages and Extents for Storage
• VLF, MiniLSN & Checkpoint

Ch 27: Capstone Project (HealthCare Domain)

• HealthCare Database
• DB & Table Design
• Data Validations
• Query Design
• Query Tuning
• Excel Analytics

Part 1: Databricks (Spark, PySpark, Big Data, Genie AI)
Ch 1: Databricks Introduction

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

Ch 2: Databricks Architecture

• Databricks Runtime (DBR)
• RDD & DAG
• Databricks Lakehouse Architecture
• Spark Compute Concepts
• Workers & Drivers
• Databricks Framework
• Databricks APIs

Ch 3: Unity Catalog

• Unity Catalog Concepts
• Metastore
• Catalogs & Schemas
• Managed & External Tables
• Storage Credentials
• External Locations
• Volumes
• GRANT / REVOKE
• Data Permissions
• Lineage, Data Governance
• Databricks Workspace UI
• Spark Table Creation

Ch 4: Spark SQL: Basics

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

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 & 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
• 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

Ch 13: Python: Modules & DataFrames

• Python Modules
• Pandas, NumPy
• DataFrame Concepts
• Handling Nulls
• Data Cleansing Concepts
• Python Functions

Ch 14: PySpark DataFrames & Transformations

• Creating PySpark DataFrames
• Reading Data Sources
• select(), filter(), withColumn()
• Handling NULL Values
• Data Type Casting
• Joins, Aggregations
• Window Functions
• Writing DataFrames

Ch 15: Medallion Architecture – 1

• Medallion Architecture
• Aggregated Data Loads
• Bronze, 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
• Data Frames, Temp Views
• Aggregated Data Loads
• Gold Data Consumption
• Raw Sources → Bronze → Silver → Gold → BI/Analytics

Ch 17: Delta Lake

• Databricks Delta Lake
• Schema Evolution
• Azure SQL DB Connections
• DataFrames, Temp Views
• Delta Table API
• MERGE / Upsert Operations
• Version History & Data 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

• Workflows & CRON
• Job Compute, Running Tasks
• Python Script Tasks
• Parameters into Notebook Tasks
• Parameters into Python Script Tasks
• Concurrent Executions, Dependencies
• Branching Control

Ch 20: PySpark: Auto Loader

• Cloud Files Architecture
• Checkpoint Configurations
• Schema Location
• Checkpoint Location
• Data Stream
• Schema Evaluation Modes
• Incremental File Ingestion
• Rescued Data Column

Ch 21: LakeFlow Declarative Pipelines

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

Ch 22: Databricks Optimizations

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

Ch 23: Databricks Security, AI

• ACL: Access Control List
• Workspace Access Control
• Catalog Security
• Schema, Volume Security
• Table Security
• Column Security

Ch 24: Databricks with Power BI

• Workspace Settings
• Access Keys (Tokens)
• Server Host
• HTTP Path
• Spark Connectors
• Databricks Connectors

Ch 25: Genie AI

• Genie AI Concepts
• Unity Catalog
• AI Components in Databricks
• Debugging Controls
• Using AI for Notebook Design

Ch 26: GitHub & Asset Bundle

• GitHub Concepts
• GIT: Main, Branches
• Asset Bundle
• Integrating GIT with Asset Bundle
• Process Integration

Ch 27: Apache Airflow

• Apache Airflow Concepts
• Airflow Operators & DAG
• Python Scripts For Airflow
• DatabricksRunNowOperator
• DatabricksSubmitRunOperator
• Airflow Jobs

Part 2: Azure Fundamentals, ADLS & IAM
Ch 1: Azure Fundamentals

• Cloud Introduction, Azure
• Cloud Implementations
• Azure Account, Subscription
• Azure ETL & DWH Resources
• Azure Storage, IoT Resources

Ch 2: Azure Storage & ADLS

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

Ch 3: Azure Storage Security

• Microsoft Entra ID
• RBAC, IAM
• Access Keys, SAS Keys
• ACL (Access Control List)
• Connection Strings

Ch 4: Azure Stream Analytics

• Azure IoT Hubs
• Azure Stream Analytics
• LIVE Data Ingestions
• SAQL Queries
• Stream Analytics Jobs
• Handling JSON Data

Ch 5: Azure Key Vaults

• Azure Key Vaults
• Azure Keys & Secrets
• Access Policies
• Secret Management
• Managed Identity

Ch 6: Azure Logic Apps

• Azure Logic Apps
• Logic Apps & Automations
• Azure Events
• Azure Triggers & Signals
• ADLS with Logic Apps

Part 3: Azure Data Factory & Synapse
Ch 1: Azure Fundamentals

• Cloud Introduction, Azure
• Cloud Implementations
• Azure Account, Subscription
• Azure ETL & DWH Resources
• Azure Storage, IoT Resources

Ch 2: Azure Deployments, Azure SQL

• Azure SQL Server Deployment
• Azure SQL DB Deployment
• Azure SQL Database (OLTP)
• Azure Firewall Rules
• Connections from SSMS Tool

Ch 3: Azure SQL DB Migrations

• Azure Storage Account
• On-Premise SQL DB, bacpac
• Azure SQL DB Migration
• SQL Server Imports from BLOB
• Migration Verifications

Ch 4: Azure Synapse (DWH)

• Synapse Pool Architecture
• Control Node, Compute Node
• DMS (Data Movement Service)
• Azure DWH Tables: Partitions
• Distributions & MAXDOP

Ch 5: Azure Data Factory (ADF)

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

Ch 6: Azure SQL DB Loads

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

Ch 7: Azure Blob Data Loads

• ADF Author & Pipeline Runs
• Azure BLOB Storage Access
• Storage Containers in ADF
• Pipeline Validation, Schedules

Ch 8: 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 9: 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 Logging

Ch 10: ADF Data Flow – 1

• Creating Data Flow Items
• Using Multiple Sinks
• Conditional Split Transformation
• Select, Sort, Union, Loops
• ADF Debug Options

Ch 11: ADF Data Flow – 2

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

Ch 12: ADF Data Flow – 3

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

Ch 13: ADF Data Flow – 4

• Get Metadata
• IF & Lookup Transformation
• Cache Lookup
• Data Validations
• Lookup Versus Joins

Ch 14: ADF Optimizations

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

Ch 15: ADF Parameters, Security

• Linked Service Parameters
• Creating Logins & Users
• Schemas, ETL Permissions
• Logins Parameters in ADF
• Dynamic Linked Services

Ch 16: SCD & ETL with Control Tables

• ADF Templates in Realtime
• Incremental Loads (SCD)
• Control Tables, Watermarks
• Lookup Activity, Delta Queries
• SP Activity & Parameters
• Pipeline Parameters, SPs

Ch 17: ADF Metrics, Alerts

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

Ch 18: Synapse Analytics

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

Ch 19: Synapse: Dedicated SQL Pools

• Creating Dedicated SQL Pools
• BLOB Data Imports
• TSQL Queries, Data Imports
• Big Data Analytics

Ch 20: Synapse: Serverless Pools

• Serverless Pools, TSQL
• Serverless Architecture
• OPENROWSET Operations
• BULK Copy Scripts
• Big Data Analytics

Ch 21: ADF with DevOps & CI/CD

• Git repositories and branching
• ADF Git integration
• Development / Test / Production environments
• Parameterized deployments
• Production release workflow

Ch 22: Azure Databricks

• Azure Databricks Deployments
• Azure Databricks Regions
• Classic Deployments
• Access connector for Databricks

Ch 23: Azure Databricks with ADLS

• Creating IAM Users
• ADLS Security for Databricks
• Creating Service Principals
• Enterprise Applications
• Client ID, Secret ID
• External Tables (Delta)

Ch 24: Azure Databricks with ADF

• Azure Databricks Connections in ADF
• Calling Notebooks in ADF
• ADF Versus Databricks
• ADF With Databricks
• End to End ETL Strategies

Ch 25: Fabric Concepts

• Fabric Architecture
• OneLake
• Lakehouse
• Warehouse
• Data Factory
• Fabric Pipelines
• Dataflow Gen2

Ch 26: Fabric Migrations

• Azure → Fabric migration scenarios
• ADF → Fabric Data Factory
• Synapse → Fabric
• ADLS → OneLake

Part 4: Real-Time Projects for Data Engineering
Real-Time Project 1: Online Travel Data Engineering Platform

Project Objective:
Build an end-to-end Azure Data Engineering solution to process, transform, and analyze Online Travel
business data from multiple sources.

Skills Gained:

• Data Ingestion & ETL Development
• Azure Data Factory Pipelines
• Data Orchestration (End to End)
• Data Lake Architecture, RBAC
• Real-Time Industry Experience
• Azure IoT, Stream Analytics
• Azure Databricks with Data Factory

Components For Project

• ADF Pipelines
• Databricks Notebooks
• Synapse Analytics
• Apache Spark Schemas
• Monitoring & Alerts
• CI/CD Deployment
• End to End Integrations
• IAM & Managed Identity

Real-Time Project 2: Retail Platform
Retail Sales Analytics Platform using Databricks (Medallion Architecture)

Project Overview

Students will build a production-style data platform using Lakehouse Architecture (Bronze, Silver, and
Gold), implementing modern ETL practices, data quality validation, incremental processing, orchestration,
optimization, and reporting.

Business Scenario

A multinational retail company receives daily data from multiple operational systems. The company wants
to build a centralized analytics platform to:

• Consolidate data from different business domains
• Improve data quality and consistency
• Track sales performance across regions
• Analyse customer purchasing behaviour
• Monitor inventory levels
• Generate business KPIs for decision-makers
• Deliver Power BI dashboards with near real-time insights

Technologies Covered

• Apache Spark
• PySpark
• Spark SQL
• Delta Lake
• Unity Catalog
• Databricks Workflows
• Git Integration
• AirFlow

Project Workflow

Sources → ADLS → Bronze → Silver → Gold → Databricks Workflows → Power BI

Part 5: Data Engineering Certifications
Databricks Data Engineer Associate Exam

• Exam Details
• Exam Dumps
• Scenario-Based Questions
• Practice Assessments

Microsoft Databricks Exam (DP-750)

• Exam Details
• Exam Dumps
• Scenario-Based Questions
• Practice Assessments

Resume Highlights
After completing this module, you can confidently showcase experience in:

• PySpark & Spark SQL
• Delta Lake
• Medallion Architecture
• ETL Pipeline Development
• Data Quality & Validation
• Unity Catalog
• Automations, Optimizations

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
• 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

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)

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 MEMBER
• 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

• 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 3: 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

• Azure SQL Database
• Azure Data Lake
• Azure Synapse
• Databricks API
• Power BI Service
• Power Pivot
• Co-Pilot

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

• Power BI Reporting
• Azure Synapse with Power BI
• Spark Schemas with Power BI
• End-to-End Azure Data Engineering Solutions

Project Flow

Source → Data Lake → Transformation → Warehouse/Lakehouse → Model → Power BI

• End-to-End Real-Time Projects & Solutions
• Medallion Architecture Implementation
• 100% Hands-on Practical Sessions
• PySpark & Spark SQL from Basics to Advanced
• Interview Preparation
• Assignments & Practice Labs
• 100% Practical • No Unnecessary Theory
• Learn Like You’re Working in a Real Company