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#DP-600

DP – 600 is the Microsoft certification for the Data Analyst using Fabric. Data Analyst is a promising job role that deals with Data, Analysis, Derived Information and drive to Infographics of enterprise content. This role also involves Data collection, data processing, and data analysis to identify trends, patterns, and relationships that can be used to solve problems and make informed decisions with ease!

Training Highlights

✅ Power BI with Fabric
✅ Fabric DWH with Power BI
✅ Fabric Lakehouse with Power BI
✅ Fabric Stream House & Power BI
✅ Dynamic Row-Level Security
✅ Data Activator, Alerts
✅ Big Data Analytics
✅ DP 600 Exam Guidance
✅ Real Time Project
✅ 1:1 Mentorship, Resume

Modules We Learn 

✅ Module 1: SQL Server TSQL (MSSQL) Queries
✅ Module 2: Power BI
✅ Module 3: Fabric Analytics, KQ
✅ Module 4: AI & CoPilot
✅ Module 5: DP-600 Guidance

Course Duration: 3 Months

DP 600 Training
Course Contents:

Module 1: SQL Server TSQL (MSSQL) Queries

Ch 1: Data Analyst Job Roles

  • Introduction to Data
  • Data Analyst Job Roles
  • Fabric Eco System
  • Fabric Data Analytics
  • DP-600 Exam Plan

Ch 2: Database Intro & Installations

  • Database Intro & Types
  • DBMS Concepts
  • SQL Server 2025 Installations
  • SSMS Tool Installation
  • Server Connections

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 Imports, Schemas

  • Data Imports with Excel
  • Order By with WHERE
  • TOP & OFFSET
  • Schemas: Creation, Usage
  • Real-world Banking Database
  • 2 Part, 3 Part & 4 Part Naming

Ch 6: Data Types & Variables

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

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: Normal Forms & ERD

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

Ch 9: Joins Queries – Level 1

  • Joins: Table Comparisons
  • Inner Join & Outer Joins
  • Cross Join & Cross Apply
  • Table & Column Aliases
  • 3 Table, 4 Table Joins

Ch 10: Joins Queries – Level 2

  • Group By & Aggregations
  • Joins with Group By
  • WHERE & HAVING
  • Sub Queries
  • Query Execution Order

Ch 11: Views & Data Analytics

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

Ch 12: Stored Procedures – Level 1

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

Ch 13: Stored Procedures – Level 2

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

Ch 14: Functions in TSQL

  • Using Defined Functions (UDF)
  • Scalar Functions in Real-world
  • Table Valued Functions
  • Parameterized Queries
  • Returns and Return

Ch 15: Window Functions, CTE

  • Date & Time Functions
  • String Functions
  • Window (Rank) Functions
  • Rank, Row_Number
  • CTEs & Data Cleanisng

Ch 16: 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 17: Transactions & ACID

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

Ch 18: Indexes Basics, Tuning

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

Ch 19: Cursors & Temp Tables

  •  IIF(), CASE Statement
  • Cube( ) and Rollup( )
  • Grouping( ) & Usage
  • Local & Global Temp Tables
  • SELECT..INTO Statement

Ch 20: Real-time Case Studies (HealthCare)

Module 2: Power BI

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

Module 3: Fabric Analytics, KQL

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
  • 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 Security & Governance

  • Workspace Security
  • Item Security & Roles
  • Adding AD Users
  • Item Security Limitations
  • MFA & Client Security
  • Data Governance Concepts

Ch 5: 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 6: Power Query Level 1

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

Ch 7: Power Query Level 2

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

Ch 8: 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 9: Stream House, KQL

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

Ch 10: KQL Query Sets

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

Ch 11: Fabric Data Activator

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

Ch 12: Model Layouts

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

Module 4: AI & CoPilot

Ch 1: Artificial Intelligence (AI)

  • Introduction to AI
  • What is Artificial Intelligence?
  • History and Evolution of AI
  • Types of AI
  • Narrow AI
  • General AI
  • Super AI
  • Real-World Applications of AI

Ch 2: AI Concepts in Real-Time

  • AI in Banking & Finance
  • AI in Healthcare
  • AI in Retail & E-Commerce
  • AI in Manufacturing
  • AI in Education
  • AI in Customer Support

Ch 3: Data Analytics with AI

  • AI-Powered Analytics
  • Traditional Analytics vs AI Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • AI for Business Intelligence
  • Pattern Detection
  • Trend Analysis
  • Forecasting
  • Anomaly Detection

Ch 4: Using AI in Power BI

  • AI Features in Power BI
  • Smart Narratives
  • Quick Insights
  • Explain the Increase/Decrease
  • AI-Powered Analytics
  • Trend Analysis
  • Forecasting

Ch 5: Power BI Visualizations

  • Choosing the Right Visual
  • KPI Cards
  • Advanced Visuals
  • Storytelling with Data
  • Dashboard Optimization

Ch 6: NLP in Power BI (Q&A)

  • Natural Language Queries
  • AI-Powered Search
  • NLP Scenarios
  • Generate Insights Automatically
  • Dynamic Data Exploration
  • AI Generated Answers
  • Dashboard Interactions

Ch 7: AI with Power Query

  • AI Features in Power Query
  • Data Profiling
  • Data Cleansing
  • AI-Based Data Preparation
  • Sentiment Analysis
  • Key Phrase Extraction
  • Clean Data Using AI
  • Automated Transformations

Ch 8: AI with DAX & Analytics

  • AI-Driven DAX
  • Time Intelligence
  • Dynamic KPIs
  • Advanced Analytics
  • Predictive Calculations
  • Trend Analysis
  • AI-Based Measures
  • Business Forecasting

Ch 9: AI with Power BI: End-to-End AI Project

  • Generate Reports using Copilot
  • Create DAX using Copilot
  • Generate Visuals using AI
  • Resume Projects

Module 5: DP-600 Guidance

  • Fabric Warehouse
  • Fabric Lakehouse
  • Fabric Semantic Models
  • Fabric Stream House
  • Fabric with Power BI
  • CoPilot Concepts
  • DP-600 Exam Guidance & Samples

What is DP-600 Job Role?

A DP-600 certified professional is an Azure Enterprise Data Analyst Associate who designs, builds, and deploys end-to-end analytics solutions using Microsoft Fabric. The role focuses on working with lakehouses, warehouses, dataflows, pipelines, data models, and Power BI to deliver enterprise-grade data analytics solutions. DP-600 professionals ensure data is integrated, secured, modeled, and visualized effectively for business insights.

What are the Job Roles of a DP-600 Certified Fabric Data Analyst?

💼 Top Job Roles:

1️⃣ Design and implement lakehouse and warehouse data models in Fabric
2️⃣ Build and manage data pipelines and dataflows for ingestion and transformation
3️⃣ Create enterprise semantic models for Power BI reporting
4️⃣ Implement security, governance, and compliance in analytics solutions
5️⃣ Optimize performance of Fabric data solutions and reports
6️⃣ Collaborate with data engineers, architects, and business stakeholders and more..!

What does our DP-600 Training course contains?

The course is carefully curated with below module:
👉🏻Module 1: MSSQL & TSQL Queries
👉🏻Module 2: Power BI

Who can join this course?

  • Freshers aspiring for Azure Database Administrator roles

  • SQL DBAs moving to cloud database administration

  • On-prem DBAs upgrading to Azure SQL solutions

  • IT professionals seeking DP-300 certification

  • Anyone wanting to manage cloud-based SQL workloads

No prior coding experience is required. All concepts are taught from scratch

What training modes are available?

Option 1:        LIVE Online Training  (100% Interactive, step by step, assignments)

Option 2:        Self Paced Videos (100% practical, step by step with concept wise assignments)

You may choose any one of these options, same curriculum!

I (Trainer) shall be available for doubts and clarifications, assignment check and review.

Why should I choose SQL School for DP-600 training?

👉🏻 Every session is Practical, Step by Step with Concept wise FAQs !!

👉🏻 100% results with on-time practice.  Daily Tasks for every session.

👉🏻 Concept wise tasks be submitted before next class for Job Waiters / Starters.

👉🏻 Concept wise tasks due for submission by Weekends for Working Professionals.

Why SQL SCHOOL?

Training Modes

Why Choose SQL School

  • 100% Real-Time and Practical
  • ISO 9001:2008 Certified
  • Concept wise FAQs
  • TWO Real-time Case Studies, One Project
  • Weekly Mock Interviews
  • 24/7 LIVE Server Access
A man smiling and giving a thumbs up while holding a notebook.
  • Realtime Project FAQs
  • Course Completion Certificate
  • Placement Assistance
  • Job Support
  • Realtime Project Solution
  • MS Certification Guidance

SQL School Fabric Data Analyst training certificate of completion issued in January 2026 with verification ID
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