SQL School

#Fabric Data Engineer With AI

Fabric Data Engineer is the latest trending job role that deals with End to End Data Warehouse design (DWH) using ETL (Extract, Transform, Load) techniques. This prominent job role also involves Big Data Analytics and Business Intelligence implementation using Spark, PySpark, Cloud Computing, TSQL and more..

Using AI and CoPilot, we can achieve optimized One Lake Structures (Warehouse, LakeHouse, Stream House, etc.. ) and laser fast analytics !

Training Schedules

S No	Time (IST, Mon - Fri)	Start Date
1	6 AM - 7 AM	Sep 2nd
2	10 AM - 11 AM	Aug 26th
3	6 PM - 7 PM	Sep 8th
4	8 PM -9 PM	Sep 15th

Fabric Data Engineer
Training Course Contents:

Brochure Download Register Now

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: Fabric Data Engineer

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
Activity Check, Schedule

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)
Data Loads to FDWH

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: Lakehouse Aggr Loads

Aggregated Data Store
Plan & Design Aggregations
Testing Aggregations
Pipelines for Data Compute
Data Copy Options
Pipeline Optimizations
Data Loads and Verification
Pipeline Execution Tests
Pipeline Monitor Check

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 ExtractionFile 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 35: Azure Synapse Migrations

Azure Synapse DWH
Azure Synapse Connections
Migrating to Fabric
Compatibility Checks
Synapse Vs Fabric Warehouse
Fabric DWH Advantages

Ch 36: DP 700 Exam Guidance
End to End Realtime Project: Ecommerce Domain

Module 3: 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 Azure Data Engineering – 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 Azure Data Engineering – 2

AI with Azure SQL Pool (DWH)
Automated Query Tuning Concepts (DWH)
AI Search Service
Real-world Considerations

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

Ch 13 : Azure AI with Azure Data Engineering – 4

AI with Azure Databricks
Notebook Implementations with AI
Automated Caching & AI
AI based Debug Options
AI based Optimizations with ETL
AI based Optimizations with DWH
AI based Troubleshooting with ETL, ELT

Ch 14: Azure AI with Azure Data Engineering – 5

AI based Optimizations with Medallion Architecture
End to End Implementations with AI

SQL SCHOOL

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

Training FAQs

Who is SQL School? How far you have been in the training services ?

SQL School is a registered training institute, established in February 2008 at Hyderabad, India. We offer Real-time trainings and projects including Job Support exclusively on Microsoft SQL Server, T-SQL, SQL Server DBA and MSBI (SSIS, SSAS, SSRS) Courses. All our training services are completely practical and real-time. CREDITS of SQL School Training Center

We are Microsoft Partner. ID# 4338151
ISO Certified Training Center
Completely dedicated to Microsoft SQL Server
All trainings delivered by our Certified Trainers only
One of the few institutes consistently delivering the trainings for more than 19+ Years online as inhouse
Real-time projects in
- Healthcare
- Banking
- Insurance
- Retail Sales
- Telecom
- ECommerce

I registered for the Demo but did not get any response?

Make sure you provide all the required information. Upon Approval, you should be receiving an email containing the information on how to join for the demo session. Approval process usually takes minutes to few hours. Please do monitor your spam emails also.

Why you need our Contact Number and Full Name for Demo/Training Registration?

This is to make sure we are connected to the authenticated / trusted attendees as we need to share our Bank Details / Other Payment Information once you are happy with our Training Procedure and demo session. Your contact information is maintained completely confidential as per our Privacy Policy. Payment Receipt(s) and Course Completion Certificate(s) would be furnished with the same details.

What is the Training Registration & Confirmation Process?

Upon submitting demo registration form and attending LIVE demo session, we need to receive your email confirmation on joining for the training. Only then, payment details would be sent and slot would be allocated subject to availability of seats. We have the required tools for ensuring interactivity and quality of our services.

Please Note: Slot Confirmation Subject to Availability Of Seats.

Will you provide the Software required for the Training and Practice?

Yes, during the free demo session itself.

How am I assured quality of the services?

We have been providing the Trainings – Online, Video and Classroom for the last 19+ years – effectively and efficiently for more than 100000 (1 lakh) students and professionals across USA, India, UK, Australia and other countries. We are dedicated to offer realtime and practical project oriented trainings exclusively on SQL Server and related technologies. We do provide 24×7 Lab and Assistance with Job Support – even after the course! To make sure you are gaining confidence on our trainings, participans are requested to attend for a free LIVE demo based on the schedules posted @ Register. Alternatively, participants may request for video demo by mailing us to contact@sqlschool.com Registration process to take place once you are happy with the demo session. Further, payments accepted in installments (via Paypal / Online Banking) to ensure trusted services from SQL School™

YES, We use Enterprise Edition Evaluation Editions (Full Version with complete feature support valid for SIX months) for our trainings. Software and Installation Guidance would be provided for T-SQL, SQL DBA and MSBI / DW courses.

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