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#Power BI With AI

Power BI is an awesome, colourful technology that can help us to understand the data, get the actual insights and report / showcase to the customers with ease. Using Power BI, we can design interactive visualizations, dashboards, apps and more.  Using AI & CoPilot Integrations, we can take Power BI to next level with eye catchy reports and improve customer engagement. Our Power BI with AI Training course from SQL School is carefully designed to give you step by step concepts with job orientation on complete Power BI Eco System with:

✅ Power BI Co-pilot & AI Integration
✅ Natural Language Queries (Q&A)
✅ AI Visuals & Smart Narratives
✅ R & Python with Power BI
✅ Real-Time Data with AI Insights
✅ Row-Level Security with AI
✅ Deployment with Fabric/Azure AI
✅ PL 300 & DP 600 Exam Guidance
✅ Real Time Project
✅ 1:1 Mentorship, Resume

Power BI With AI Training
Course Contents:

Module 1 : Microsoft SQL (TSQL)

Ch 1: SQL SERVER INTRODUCTION

  • Database Introduction
  •  Types of Databases
  •  Need for & ETL, DWH
  •  BI Implementations
  •  SQL Server Advantages
  •  Version, Editions of MSSQL
  •  Data Analyst Job Roles

Ch 2: SQL SERVER INSTALLATIONS

  • SQL Server 2019, 2017
  • SSMS Tools Installation
  • Database Engine (OLTP)
  • SCM, Configuration Tools
  • Instance Types, Uses
  • Authentication Modes
  • Collation, File Stream

Ch 3: SQL BASICS – 1

  • Need for Databases, Tables
  • Need for SQL Commands
  • DDL, DML & DQL Statements
  • Database Creation @ GUI
  • Data Operations @ GUI
  • Session ID, SQL Context
  • DB, Tables, Data @ SQL

Ch 4: SQL BASICS – 2

  • DDL Variants in MSSQL
  • DML Variants in MSSQL
  • INSERT & INSERT INTO
  • SELECT & SELECT INTO
  • Basic Operators in SQL
  • Special Operators in MSSQL
  • ALTER, ADD, TRUNCATE, DROP

Ch 5: Data Imports, Schemas

  • Data Imports with Excel
  •  ORDER BY & UNION
  • UNION ALL For Sorting Data
  •  Creating, Using Schemas
  •  Real-world Banking Database
  •  Table Migrations @ Schemas
  •  2 Part, 3 Part & 4 Part Naming

Ch 6 : Constraints, Index Basics

  • Need for Constraints, Keys
  •  NULL, NOT NULL, UNIQUE
  •  Primary Key & Foreign Key
  •  RDBMS and ER Models
  •  Identity Property, Default
  •  Clustered Index, Primary Key
  •  Non Clustered Index, Unique

Ch 7: Joins & Views Basics

  • JOINS: Purpose. Inner Joins
  • Left / Right / Full Outer Joins
  • Cross Joins, Query Tuning
  • Creating & Using Views
  • DML, SELECT with Views
  • RLS : WITH CHECK OPTION
  • System Views & Metadata

Ch 8: Functions(UDF), Data Types

  • Using Functions in MSSQL
  •  Scalar Value Functions
  • Inline & Multiline Functions
  • Date & Time Functions
  • String, Aggregate Functions
  • Data Types : Integer, Char, Bit
  • SQL Variant, Timestamp, Date

Ch 9: Stored Procedures,Models

  • Stored Procedures & Usage
  • Creating, Testing Procedures
  • Encryption, Deferred Names
  • SPs for Validations, Analysis
  • System SPs, Recompilation
  • Normal Forms & Types
  • Data Models, Self-References

Ch 10: Triggers, Temp Tables

  • Need for Triggers
  • DDL & DML Triggers
  • Using Memory Tables
  • Data Replication, Automation
  • Local & Global Temp Tables
  • Testing & Using Temp Tables
  • SELECT .. INTO & Bulk Loads

Ch 11: DB Architecture, Locks

  • Planning VLDBs : Files, Sizing
  • Filegroups, Extents & Types
  • Log Files : VLF, Mini LSN
  •  Table Location, Performance
  • Schemas, Transfer, Synonyms
  • Transactions Types, Lock Hint
  •  Query Blocking Scenarios

Ch 12 : Cursors & CTEs, Links

  • Cursors : Realtime Use
  • Fetch & Access Cursor Rows
  • CTEs for SELECT, DML
  • CTEs: Scenarios & Tuning
  • Linked Servers, Remote Joins
  • Linked Servers: MSDTC, RPC
  • Tuning Remote Queries

Ch 13: Merge, Upsert & Rank

  • Need for Merge in ETL
  • Incremental Loads with SQL
  • MERGE and RANK Functions
  • Window Functions, Partition
  • Identify, Remove Duplicates

Ch 14: Grouping & Cube

  • Group By & HAVING
  • Cube, Rollup & Grouping
  • Joins with Group By
  • 3 Table, 4 Table Joins
  • Query Execution Order

Ch 15: Self Joins, Excel Analysis

  • Self Joins & Self References
  •  UNION, UNION ALL
  •  Sub Queries with Joins
  •  IIF, CASE, EXISTS Statements
  •  Excel Analytics, Pivot Reports

Module 2: Power BI

Ch 1 : Power BI Introduction

  • Reporting Basics & Types
  • Interactive,Analytical Reports
  • Paginated Reports (RDL)
  • Power BI Eco System
  • Power BI Tools,Service,Server
  • Need for Power Query (M)
  • Need for DAX & Cloud

Ch 2: Power BI Basic Reports

  • Power BI Desktop Installation
  • Basic Report Design (PBIX)
  • Data View, Data Models
  • Data Points, Aggregations
  • Focus Mode, Spotlight, Exports
  • ToolTip, PBIX and PBIT
  • Visual Interactions & Edits

Ch 3 : Grouping, Hierarchies

  • Creating Groups in Power BI
  • Groups : Creation & Usage
  • Group Edits Options
  • Bins & Bin Size, Bin Count
  • Hierarchies: Creation, Use
  • Drill Down, Drill Up
  • Conditional Drill Down

Ch 4 : Visual Sync, Filters

  • Slicer & Single Select
  • Multi Select Options
  • Integer, Character Slicers
  • Visual Sync with Slicers
  • Filters: Visual, Page, Report
  • Drill Thru Filters & Usage
  • Basic, Top & Advanced
  • Clear Filter Options, Resets

Ch 5 : Bookmarks, Big Data

  • Bookmarks Creation & Usage
  • Visual Interactions, Bookmarks
  • Images : Actions, Bookmarks
  • Big Data Access with Power BI
  • Storage Modes: Direct Query
  • Import & Performance Impact
  • Formatting & Data Refresh
  • Summary, Date Time Formats

Ch 6 : Power BI Visualizations

  • Chart and Bar Visuals
  • Line and Area Charts
  • Maps, TreeMaps, HeatMaps
  • Funnel, Card, Multrow Card
  • PieCharts & Settings
  • Waterfall, Sentiment Colors
  • Scatter Chart, Play Axis
  • Infographics, Classifications

Ch 7 : Power Query Level 1

  • Power Query (Mashup)
  • ETL Transformations in PBI
  • Power Query Expressions
  • Table Combine Options
  • Merge, Union All Options
  • Table Transformations

Ch 8 : POWER QUERY LEVEL 2

  • Any Column Transformations
  • String / Text Transformations
  • Numeric Analytics & Mashup
  • Date Time Transformations
  • Add Column Transformations
  • Expressions and New Columns

Ch 9 : POWER QUERY LEVEL 3

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

Ch 10 : Power BI Cloud – 1

  • Power BI Cloud Concepts
  • Workspace Creation, Usag
  • Report Publish & Edits
  • Semantic Models in Realtime
  • Dashboard Creation, Usage
  • Clone, Share, Subscribe
  • Q&A, Lineage, Settings

Ch 11 : Power BI Cloud – 2

  • Data Gateways, Data Refresh
  • Data Source Configurations
  • Data Refresh & Scheduling
  • Gateway Optimizations
  • Semantic Model Optimizations
  • Report Optimizations
  • Dashboard Optimizations

Ch 12 : Power BI Cloud – 3

  • Power BI Apps, Shares
  • App Sections & Options
  • App Updates, Security
  • Excel Analytics
  • Data Explorer Option
  • Sharing, Subscriptions
  • Alerts, Metrics, Insights

Ch 13 : Report Server & DAX

  • Power BI Report Server
  • Report Database, TempDB
  • Web Service & Server URL
  • Paginated Reports (RDL)
  • Report Builder Tool Usage
  • DAX : Purpose, Realtime Use

Ch 14: DAX Level 2

  • DAX Measures Creation, Use
  • DAX Functions: IIF, ISBLANK
  • SUM, CALCULATE Functions
  • DAX Cheat Sheet : Examples
  • Quick Measures in Power BI
  • Running Totals, Filters

Ch 15 : DAX Level 3

  • Star Rating Calculations
  • Data Models & DAX
  • Star & Snowflake Schemas
  • Dimensions, Fact Tables
  • DAX Expressions & Joins
  • DAX Variables, Usage

Ch 16 : DAX Level 4

  • Dynamic Report with DAX
  • SELECTED MEMEBER
  • Time Intelligence with DAX
  • PARALLELPERIOD, DATE
  • DAX with Big Data
  • Big Data Analytics

Ch 17 : Realtime Project – Requirements

  • Customer Requirement
  • Scope of the Project
  • End User Take Aways
  • Implementation Phases
  • Data Sources & Types
  • Data Sheets, Project Planning

Ch 18 : Realtime Project – Implementation

  • Excel Data Sources
  • SQL Database Sources
  • Azure SQL DB Sources
  • AVRO, JSON, PDF Sources
  • Reports, Dashboards, Apps
  • Project Solution & FAQs

Module 3: Azure AI & CoPilot

Ch 1 : Fundamental AI Concepts

  • AI: Artificial Intelligence
  • Real-time Implementation
  • Understand Computer Vision
  • Understand Natural Language Processing
  • Document Intelligence and Knowledge Mining
  • Understand Generative AI
  • Challenges and Risks with AI
  • Understand Responsible AI

Ch 2: Fundamentals of Machine Learning

  • Machine Learning Introduction
  • Machine Learning Components
  • Types of Machine Learning
  • Regression, Binary Classification; Multiclass Classification
  • Clustering, Deep Learning
  • Azure Machine Learning

Ch 3 : Fundamentals of Azure AI services

  • AI Services on Azure platform
  • Create Azure AI Service Resources
  • Use Azure AI services
  • Understand Authentication for Azure AI services
  • Exercise – Explore Azure AI Services

Ch 4 : Computer Vision

  • Images and image processing
  • Machine learning for computer vision
  • Azure AI Vision
  • Exercise – Analyze images in Vision Studio

Ch 5 : Natural Language Processing

  • Understand Text Analytics
  • Text Analysis in Azure
  • Exercise – Analyze text with Language Studio

Ch 6 : Document Intelligence and Knowledge Mining

  • Introduction to Document Intelligence
  • Knowledge Mining
  • Explore capabilities of document intelligence
  • Receipt Analysis on Azure
  • Exercise – Extract from data in Document Intelligence Studio

Ch 7 : Generative AI

  • What is generative AI?
  • What are language models?
  • Using language models
  • What are copilots?
  • Considerations for Copilot prompts
  • Extending and developing copilots
  • Exercise – Explore Microsoft Copilot

Ch 8 : Generative AI in Azure

  • Generative AI – Capabilities within AI in Azure
  • Azure Implementation of Gen AI
  • Processing Images, Codes and more

Ch 9 : AI 900 Exam Guidance

  • Describe Artificial Intelligence workloads and considerations
  • Describe fundamental principles of machine learning on Azure
  • Describe features of computer vision workloads on Azure
  • Describe features of Natural Language Processing (NLP) workloads on Azure
  • Describe features of generative AI workloads on Azure

Ch 10 : Azure AI with Data Analytics – 1

  • Implementing AI in Cloud
  • Co-Pilot Concepts in Big Data
  • AI with Azure
  • AI with Azure SQL Database
  • Automated Query Tuning Concepts (OLTP)

Ch 11 : Azure AI with Data Analytics – 2

  • AI with Power BI
  • CoPilot with Power BI – Power Query
  • CoPilot with Power BI – Cloud
  • CoPilot with Power BI – DAX

Ch 12 : Azure AI with Azure Data Engineering – 3

  • AI with Azure Storage Account
  • ADLS Concepts and AI Implementations
  • AI Search Service with ADLS
  • Text Data Handling with AI

SQL SCHOOL

24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.

Azure Data Engineer With AI Training FAQ's

What is Azure Data Engineer with AI Job Role?

An Azure Data Engineer with AI specialization is a modern data professional responsible for designing, building, and maintaining end-to-end data solutions in Azure that integrate AI and machine learning capabilities. This role combines traditional data engineering tasks like data ingestion, transformation, storage, and performance tuning with advanced AI integrations such as Azure Cognitive Services, OpenAI APIs, Azure ML, and SynapseML to support intelligent analytics and predictive applications.

What are the Job Roles of an Azure Data Engineer with AI?

💼 Top Job Roles:

1️⃣ Build and manage scalable data pipelines using Azure Data Factory, Synapse & Data Lake
2️⃣ Integrate AI APIs (OpenAI, Azure Cognitive Services, Azure ML) into data workflows
3️⃣ Implement data models, warehouses, and real-time data solutions
4️⃣ Develop intelligent dashboards, semantic layers, and custom ML integrations
5️⃣ Ensure security, governance, and compliance of AI-powered data systems
6️⃣ Collaborate with data scientists, BI developers, and business stakeholders and more..!

What does our Azure Data Engineer with AI Training course contain?

The course is carefully curated with below module:
👉🏻Module 1: Microsoft SQL (TSQL)
👉🏻Module 2: Azure Data Engineer with AI
👉🏻Module 3: Azure AI, Co-Pilot

Who can join this course?

  •  Freshers interested in cloud BI and analytics
  • SQL/BI professionals expanding to AWS BI tools
  • ETL developers moving to AWS cloud solutions
  • Data analysts aiming for AWS BI certifications
  • Anyone looking to build enterprise BI solutions on AWS

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 Azure Data Engineer With AI 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 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
  • Realtime Project FAQs
  • Course Completion Certificate
  • Placement Assistance
  • Job Support
  • Realtime Project Solution
  • MS Certification Guidance