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#Fabric Data Engineer

Fabric Data Engineer 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.

Training Highlights

✅ Cloud ETL, DWH with Big Data Analytics
✅ OneLake & Lakehouse for Unified Storage
✅ Fabric Data Factory for ETL
✅ Dataflows Gen2, Self-Service Data Prep
✅ Delta Lake, Delta Tables with Big Data
✅ ETL Notebooks with PySpark, TSQL
✅ Realtme IoT with Eventstreams
✅ CI/CD with Fabric Git Integrations
✅ 1:1 Mentorship, Interview Guidance

Modules We Learn:

✅ Module 1: SQL Server (MSSQL) & T-SQL
✅ Module 2: Fabric Data Engineering
✅ Module 3: DevOps for Fabric Data Engineering
✅ Module 4: Real-Time Project
✅ Module 5: Data Engineer Certifications

Course Duration: 2.5 Months

Fabric Data Engineer Training by Sai Phanindra Tholeti | Microsoft Fabric and Azure Data Engineering

Fabric Data Engineer
Course Contents:

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Module 1: SQL Server TSQL (MS SQL) Queries

Ch 1: SQL Database Job Roles

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

Ch 2: Database Intro & Installations

SQL Server Installations
Instance Concepts
Authentication Types
Authentication Modes
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
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: Normal Forms & ERD

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

Ch 9: Joins Queries – Level 1

Joins: Table Comparisons
Inner Join & Outer Joins
Cross Join & Cross Apply
Table Combination
Table & Column Aliases

Ch 10: Joins Queries – Level 2

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

Ch 11: Sub Queries

Distinct & Union, Union All
Sub Queries Concept
Sub Queries & Aggregations
Joins with Sub Queries
Correlated Queries

Ch 12: Views & Data Analytics

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

Ch 13: Stored Procedures – Level 1

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

Ch 14: Stored Procedures – Level 2

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

Ch 15: Functions – Level 1

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

Ch 16: Functions – Level 2

Aggregated Functions
Date & Time Functions
String Functions
Window Functions
Rank, Row_Number
DenseRank, Partition By

Ch 17: 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 18: Transactions & ACID

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

Ch 19: Indexes Basics, Tuning

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

Ch 20: CTEs & Tuning

Common Table Expression
Creating and Using CTEs
CTEs, In-Memory Processing
IIF(), CASE Statement
Cube( ) and Rollup( )
Sub Totals & Grand Totals
Grouping( ) & Usage

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: Temp Tables

Local Temp Tables
Global Temp Tables
Testing Temp Tables
SELECT..INTO Statement
Bulk Copy Operations

Ch 23: SQL Server Architecture

Network Protocols
Query Execution Engine
Parser, Compiler, Checkpoint
SQL Manager, DB Manager
Storage Engine, Locks
SQL OS Components

Ch 24: Real-Time SQL Server Case Studies

✅ Healthcare Management System

Patient Records Management
Doctor Appointment Scheduling
Billing & Insurance Processing
Medical Reports Analysis

✅ E-Commerce Database

Customer & Product Management
Order Processing
Inventory Tracking
Sales Reporting

✅ Banking & Financial Systems

Customer Accounts
Transaction Processing
Loan Management
Fraud Detection Queries

Module 2: Fabric Data Engineering

Part 1: Fabric Concepts, DWH & Fabric Data Factory

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 OneLake 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
SSMS Connections

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
Save As table, Save As View

Ch 6: 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 7: Fabric Statistics

Query Engine Options
Statistics Types
Leverage Statistics
Auto, Manual Statistics
Update Statistics
Statistics Consistency
Statistics Lists & Reports

Ch 8: 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 9: 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 10: Fabric Security

Workspace Security
Warehouse Security
Item Security & Roles
Adding AD Users
Item Security Limitations
MFA & Client Security
Ch 11: Fabric Copy Job
ETL Implementation Options
Copy Job Item
Data Loads with Copy Job
Full Loads
Testing Copy Jobs

Ch 12: Fabric Copy Job

Incremental Loads with Copy Job
Business Key Concept
DWH: Data Storage
Testing Initial Loads
Testing Incremental Loads
Copy Job Limitations

Ch 13: Fabric Data Factory

Need for Fabric Data Factory
ETL Operations in FDF
Data Sources, Transformations
Activities and Connections
Data Destinations (Sinks)
Creating Pipelines

Ch 14: Fabric Pipelines Design

Creation Options for Pipelines
Azure SQL DB Data Loads
Creating Data Sets
Copy Command Usage
Run ID & Monitoring
Pipeline Creation, Verification

Ch 15: Data Loads with Azure

Azure Data Lake Storage (ADLS)
Azure BLOB Containers
Fabric Data Loads From Azure Files
Fabric Warehouse with Azure
Run IDs and Activity
Compressions & Advantages

Ch 16: ETL Staging

Staging: Advantages
Caching & Storing Concept
Staging Types in Fabric
Workspace & External
External Stages in Pipelines
Pipeline Trigger, Monitor

Ch 17: 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 18: Fabric Incremental Loads – 1

Upsert (Incremental Loads)
Business Key Concept
SCD: Slowly Changing Dimension
Full Loads Vs Incr Loads
Testing Incrementing Loads
Pipeline Execution Tests

Ch 19: Fabric Incremental Loads – 2

Control Tables
Watermark Columns
Lookup Activity
Stored Procedure Activity
Parameters & Runs
Concurrency & Batch Count

Ch 20: OnPrem Gateways

Need for On-Premise Gateway
Installing & Configuring
Authentication, Usage
On-Premise Connections
Pipelines for Data Loads
Warehouse Data Storage
Data Refresh with Gateways

Ch 21: Data Factory Pipeline Tuning

Intelligent Throughput
DOCP & Optimizations
Staging & In-Memory
Reliable Logging
Spark Compute Options
Concurrent Connections
ETL Partitions in Real-world

Part 2: Fabric Data Flow, Lake House

Ch 1: Fabric Lakehouse

Fabric Lakehouse Architecture
Files and Tables Storage
Direct Lake & AI
Creating Lakehouse
Azure SQL Database Source
UI: Limitations

Ch 2: Lakehouse File Loads

Creating Lakehouse
Incremental Refresh
Computed Tables
Reusable Transformations
Scheduling
Monitoring
Best Practices

Ch 3: Power Query Level 1

Power Query Concept
Fabric Lake House
ETL, ELT Process with AI
Data Combine: Union, Append
Duplicate / Reference Queries
Warehouse Data Loads

Ch 4: Power Query Level 2

Table Transformations
Group By, Transpose
Header Row Promotion
Reverse Rows, Count Rows
Any Column Transformations
Data Type, Fill & Pivot

Ch 5: Power Query Level 3

Text Transformations
Format, SubString
Number Transformations
Date Time Transformations
Add Column Transformation

Ch 6: Power Query Level 4

Column From Examples
Conditional Column
Index Transformation
Duplicate Rows, Errors
Advanced Editor

Ch 7: Power Query Level 5

ETL Parameters
Big Data Access
Static Parameters
Dynamic Parameters
List Queries
M Language Expressions

Ch 8: Stream House, KQL

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

Ch 9: KQL Query Sets

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

Ch 10: Fabric Data Activator

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

Ch 11: Mirror Database

Need for Mirror Databases
Configure Mirror Databases
Data Replication
Schema Replication
Connections & Usage
Mirror DB Practical uses

Part 3: Python, PySpark, DWH

Ch 1: Fabric Notebooks

Need for Notebooks
Fabric Notebook Types
Creating Environment
Creating Spark Clusters
Standard, High Concurrency
Magic Command
Freeze Cells

Ch 2: Spark SQL – 1

Spark SQL Notebooks
Creating Schemas
Delta Tables
Parquet Tables
Spark Joins
Spark SQL Aggregations
Data Partitioning
Union, Views in Spark
Dropping Objects

Ch 3: Spark SQL – 2

Math, Sort Functions
String, Date Time Functions
Conditional Statements
Data Recovery & Undo
Version Number
Describe Extended

Ch 4: Python Intro & Print

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

Ch 5: Python Variables

Defining Variables
Using Variables
Printing Variables
Display Variables
Variable Types
Multi Value Variables
If … Else Statement

Ch 6: Python Operators

Integer Operators
String Operators
Arithmetic Operators
Assignment Operators
Comparison Operators
Formatted Strings
Indexing Operators
ELIF, ELSE IF Statements

Ch 7: Python Data Types

Python Data Types
Integer / Int Data Types
Float, String Data Types
List Data Type
List Items, Indexes
Tuple Data Type
Dictionary Data Type

Ch 8: Python Dataframes

Pandas Module (Python)
Dataframes from Lists
Dataframe from Dict
Pandas Dataframes
Dataframe print, display

Ch 9: Python Dataframes Transformations

Append
Append with NoIndex
Merge with ON, KIND
spark.read.csv()
spark.read.format()

Ch 10: Medallion Architecture

Bronze, Gold and Silver
Raw Data
Data Preparation (Prepping)
Temporary Views
Big Data Analytics

Ch 11: PySpark: Medallion Loads 1

Reading from Volumes
Dataframes, Temp Views
Data Prep (Silver)
Filtering DataFrame Records
Removing Duplicate Records
Sorting and Limiting Records
Spark SQL Dataframes
Gold Layer Implementation
Testing Aggregated Loads

Ch 12: PySpark: Medallion Loads 2

Azure SQL DB Connections
SQL Queries in PySpark
Data Prep (Silver)
Filtering Null Values
Grouping and Aggregating
Spark SQL Dataframes
Gold Layer Implementation
Notebook Utilities

Ch 13: PySpark: SCD

Slowly Changing Dimension
Merge Into Statement
Error Handling
Merge with OLTP Data Sources
Change Data Feed

Ch 14: PySpark: Widgets

Notebook Parameters
Text Widgets
Manual Executions
Parameters & JSON
Notebook Schedules, Retry
Modular Notebook Design
Notebook Chaining

Ch 15: LakeHouse Architecture Optimizations

Delta Log
Delta Versioning
Partition Strategy
Small File Problem
Adaptive Query Execution
Explain Plan, Spark UI
mssparkutils

Ch 16: LakeHouse Optimizations

VACUUM, OPTIMIZE
ZORDER
Broadcast Join
Repartition, Coalesce
Cache, Persist
Shuffle Optimization

Ch 17: Semantic Models

Creating Semantic Model
Spark SQL: DDL, DML
Adding Refences, Keys
Using Model Layouts

Ch 18: Fabric Security

Workspace Security
Lakehouse Security
Notebook Security
Security Principals
Authentication Options
MFA (Multi Factor Authentication)

Module 3: DevOps for Data Engineers, AI

Ch 1: GitHub Concepts

Creating Github Account
GIT Project Concept
GIT Project Creation
GIT: Main, Branches
GIT Credentials
Connecting with ADF
Connecting with Databricks

Ch 2: DevOps For Fabric Data Engineers

Fabric Git Integration
Git Branches
Workspace Sync
Commit
Pull
Merge
Conflict Resolution

Ch 3: Fabric AI

Copilot
AI Functions
AI Skills
AI Prompt Builder
AI Insights
Semantic Search

Module 4: Retail & E-Commerce Analytics Platform using Microsoft Fabric

Project Objective:

Build a complete enterprise-grade Data Engineering solution using Microsoft Fabric that ingests data from multiple source systems, transforms it using Medallion Architecture, stores it in OneLake and Warehouse, and serves analytics to Power BI dashboards. Students will implement the complete project exactly as done in enterprise organizations.

Business Scenario

A multinational e-commerce company wants to centralize sales, customers, products, orders, inventory, logistics and payment data into Microsoft Fabric. Current Problems:

Data scattered across multiple systems
Manual reporting
Slow dashboards
No real-time insights
No centralized data platform

Goal
Create a scalable Fabric Data Platform capable of processing millions of records daily.

Technologies Used:

Fabric Data Factory (ADF)
Fabric One Lake
Fabric Warehouse
Fabric LakeHouse
Fabric StreamHouse
Data Lake Gen2
Semantic Models
REST APIs
Azure SQL Database
Azure Data Lake
CoPilot (AI)

Skills Gained

Fabric Pipelines
Fabric Notebooks
Fabric Data Flow
Semantic Models
Medallion Architecture (Bronze/Silver/Gold)
Real-Time Industry Experience
Fabric APIs (REST, Workspace)

Components For Project (From Resume Perspective):

Source Systems
Bronze
Silver
Gold
ADF Pipelines
Synapse Analytics
PySpark
Power BI Reporting
Monitoring
Alerting
CI/CD
Deployment
End to End Integrations

Module 5: Fabric Data Engineer Certification

Fabric Data Engineer (DP 700)

Exam Q & A, Scenarios
150+ practice questions
Mock tests
Case studies
Scenario-based labs
Weekly DP-700 quizzes

What is the Fabric Data Engineer course and who can join?

This course is designed for Data Engineers, BI Developers, Cloud Engineers, SQL Developers, and professionals who want to work with Microsoft Fabric, Lakehouse, Warehouses, Data Engineering Pipelines, and AI-powered analytics.

What are the prerequisites for learning Fabric Data Engineering?

Basic SQL knowledge is helpful, but not mandatory. The program includes MSSQL + TSQL fundamentals before moving into Fabric components.

What modules are included in the Fabric Data Engineer course?

Module 1: MSSQL & TSQL (3 Weeks)
Module 2: Fabric Data Engineering (6 Weeks)
Module 3: Power BI with AI (6 Weeks)
DP-700 Exam Guidance is also included

Does this course include real-time projects?

Yes. The program includes a mini SQL project, one Fabric real-time project, and one Power BI project, including end-to-end pipeline implementation in an E-commerce domain.

What is Microsoft Fabric and why is it important?

Microsoft Fabric is an end-to-end analytics platform combining Data Engineering, Data Factory, Data Science, Power BI, Real-time Analytics, and Storage into a single unified service. It provides better performance, cost optimization, and simpler data architecture.