#Fabric Data Factory

Fabric Data Factory is a powerful and advancedq cloud-based data integration service which allows developers to create, schedule, and manage data pipelines for ETL and data movement created by Microsoft. Mastering in Azure Data Factory can land you in Data Engineer, ETL Developer, and BI Specialist job roles

Training Schedules

S No	Time (IST, Mon - Fri)	Start Date
1	6 AM - 7 AM	Sep 30th
2	8 PM -9 PM	Sep 22nd

Fabric Data Factory
Training Course Contents:

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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: Azure Data Engineer

Ch 1: ETL, DWH Introduction

Database Introduction Data Warehouse (DWH)
Data Engineering Work Flow
Cloud Concepts: IaaS, PaaS
SaaS & Azure Cloud Concepts
Azure Resources & Groups
Storage, ETL, IoT Resources

Ch 2: Azure Intro, Azure SQL

Azure SQL Server, SQL DBA
Azure SQL Database (OLTP)
Azure SQL Pool (DWH)
Connections from SSMS Tool
Connections from ADS Tool
Pause / Resume SQL Pool
Source Data Configurations

Ch 3: Azure Synapse (DWH)

Synapse Pool Architecture
Control Node, Compute Node
DMS & Partitioned Tables
Creating Tables with TSQL
Distributions: RR, Hash, Repl
Big Data Loads with TQL
Important DMFs & DMVs

Ch 4: Azure Data Factory (ADF)

Need for ADF & Pipelines
Linked Services & IRs
Datasets, Pipelines, Triggers
Copy Data Activity & CDT
Data Loads Pipelines, DTUs
Pipeline Monitoring, Edits

Ch 5: ADF Incremental Loads – 1

File Incremental Loads
Storage Account, Data Lake
Binary Copy, Schema Drift
Staging Concept in ADF
DOCP, Logging & Consistency
Polybase Concept & Tuning

Ch 6: ADF Incremental Loads – 2

Implement SCD with ADF
Self-Hosted IR: Realtime Use
On-premise Data: Incr Loads
Copy Method: Upsert, Keys
Staging & ADF Optimizations
Pipeline Runs, Activity IDs

Ch 7: ADF Data Flow – 1

Data Flow Transformations
Spark Clusters for Debugging
Optimized Clusters, Preview
Conditional Split, SELECT
Sort, Union Transformations
Pipelines with Data Flow

Ch 8: ADF Data Flow – 2

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

Ch 9: ADF Data Flow – 3

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

Ch 10: Synapse Analytics – 1

Azure Synapse Analytics
Dedicated SQL Pools
TSQL: Stored Procedures
Synapse Pipelines, Tuning
SP Activity in Pipelines, Jobs
Comparing ADF & Synapse

Ch 11: Synapse Analytics – 2

Serverless Pools in Synapse
TSQL Scripts with Serverless
ADLS Data Imports & ELT
Synapse Aggregation, Analytics
Synapse Optimizations
Synapse Security & Logins

Ch 12: Synapse Analytics – 3

Apache Spark Pool & Usage
Synapse Analytics with Pools
PySpark Staging, Aggregations
Spark Queries & Python ETL
Python Notebooks, Pipelines
Integrating Python with DWH

Ch 13: Parameters, SCD & ETL

ADF Templates in Realtime
Table Incremental Loads
Control Tables, Watermarks
Pipeline Parameters, SPs
Dynamic Data Sets, SCD

Ch 14: CDC @ ETL, ELT & Tuning

Using CDC in ADF
Control Tables (CT): Upserts
Handling Inserts, Updates
SCD Type 1 & Type 2
ADF, Synapse: Limitations

Ch 15: Azure Intro & Storage

Storage, ETL, IoT Resources
Azure Storage Components
Azure Storage Account, HNS
Azure Data Lake Storage
Azure Storage Explorer Tool
Storage Explorer Config
Storage Account Properties

Ch 16: Azure Storage Operations

BLOB Storage: Containers
Storage Browser, Explorer
File & Folder Uploads, Edits
Azure Tables: Row Key
Partition Key, Timestamp
Use Cases of BLOB Storage
Use Cases of Azure Tables

17: Azure Storage Security

Realtime use of Keys
Access Keys & Admin Access
SAS Keys Generation, Ips
Creating, Using Entra Users
Azure AD Users, Groups
IAM & RBAC with Entra Users
ACLs and ADLS Security

Ch 18: Azure SQL DB Migrations

On-Premise SQL DB bacpac
Azure SQL Deployment
Azure Storage from SSMS
Azure SQL DB Migration
Migration Verifications
Testing Migrations in SQL

Ch 19: Azure Stream Analytics

Azure IoT Hubs & Devices
APIs with Connection Strings
Azure Steam Analytic Jobs
Inputs, Outputs, SAQL Query
LIVE Feed: JSON, AVRO Files
Watermark & LIVE Stats

Ch 20: Azure Stream Analytics

Azure IoT Hubs & Devices
APIs with Connection Strings
Azure Steam Analytic Jobs
Inputs, Outputs, SAQL Query
LIVE Feed: JSON, AVRO Files
Watermark & LIVE Stats

Ch 21: Azure Key Vaults, Alerts

Azure Encryptions @ REST
Azure Key Vaults & Keys
SMK & CMK Encryptions
Azure Metrics: Ingress
Egress, E2E Latency Issues
Performance Tuning Options

Ch 22: Azure Storage Optimization

BLOB Types & Content Types
Hot, Cool, Cold, Archive Types
Creating, Using Access Policies
Immutable Storage, Rotation
Containerization, Indexing
Replication: LRS, ZRS, RA-GRS

Ch 23: Azure Pricing, Functions

Azure Logic Apps: Usage
Log Apps Usage in ETL
Snapshots, Azure Functions
Azure Functions Realtime Use
ETL & DWH with Functions
Azure Resource Pricing

Ch 24: Azure Big Data & Spark

Azure Big Data & Spark
Azure ETL & DWH Databases
Azure Spark, HIVE Metastore
Azure Databricks Service
Spark Cluster (Personal)
Unity Catalog & Azure VM

Ch 25: Spark Cluster Operations

DBFS: Flat File Imports
Table Conversions using GUI
Spark Clusters: Table Creations
Basic Transformations in Spark
SQL Notebooks: Creation
Default DB Queries, Cloning

Ch 26: Python & PySpark, ETL

Python Fundamentals
Python Data frames: ETL
Python for Big Data, Pandas
Python Notebooks, Views
Aggregated Loads to Spark
Spark DB Creations, Tables

Ch 27: PySpark & ADLS, Widgets

Creating Spark Databases
Spark Tables, Catalog Info
PySpark with ADLS Storage
Using Widgets for ADLS Keys
PySpark Variables & Widgets
Using Variables in Functions
Spark SQL with Control Text
Using Variables in Spark SQL

Ch 28: ADB Jobs, Delta Tables

Azure Databrick Jobs
Azure Workflows & Tasks
Notebook Schedule Options
Continuous Jobs, Notifications
Delta Tables & Data Cleansing
SCD (Merge Into), Contact, etc.
Creating, Using Data frames
Multi Data frame Joins

Ch 29: Scala Notebooks & ETL

Scala Notebooks: Purpose
Aggregated Data Loads
Incremental Data Loads
Widgets & Jobs with Scala
Python Versus Scala
Converting Python to Scala
JVM Benefits, SQL DB Conn”
SQL DB Loads with Scala

Ch 30: Databricks Architecture

Azure Databricks Services
Cluster Components & DBFS
RDD, DAG, Photon, Spotlight
Spark Partitioned Tables
Cluster Manager: Spark Jobs
Databricks Runtime (DBR)
Databricks Security
Workspace Security
Notebook & Job Security

Ch 31: Medallion Architecture

Medallion Architecture in ETL
DWH Data Loads & Incr Loads
Bronze, Silver & Gold Data
Processing Raw Data Files
Data Cleansing, Formatting
Aggregation Advantages
DBES & Node Architecture
Unity Catalog Concept
LUNs and Unity Catalog

Ch 32: Delta LIVE Tables (DLT)

Creating Delta LIVE Tables
DLT Pipelines in ETL, DWH
Automated Incr Loads
Control Tables, Timestamp
SCD Type 1 with DLT
SCD Type 2 with DLT
Automated Merge Into Stmt
Delta Tables Vs DLT
Merge Into Vs DLT Pipeline

Module 3: 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 Phase 1

Project Requirement Spec
Understanding Data, Formats
Report Pattern Design
Report Design & Modelling
Power Query, DAX, Insights
Analytical Reports in Cloud

Ch 18 : Realtime Project Phase 2

Complete Project Solution
Project FAQs, Key Roles
Real-world Considerations
Power BI Admin Concepts
Resume Points, FAQs
PL 300 Exam Guidance

SQL SCHOOL

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

Fabric Data Factory Training FAQ's

What is Fabric Data Factory Job Role?

A Fabric Data Factory professional is responsible for designing, building, and orchestrating end-to-end data pipelines within the Microsoft Fabric platform. This role involves ingesting, transforming, integrating, and managing data using Fabric Data Factory, while ensuring scalability, performance, and security. Fabric Data Factory engineers work across lakehouses, warehouses, and dataflows, enabling seamless data movement and processing for analytics and reporting.

What are the Job Roles of an Fabric Data Factory?

💼 Top Job Roles:

1️⃣ Design and develop data pipelines using Fabric Data Factory
2️⃣ Create and manage ETL and ELT processes across hybrid data sources
3️⃣ Build dataflows and integrate data into lakehouse/warehouse structures
4️⃣ Automate workflows with triggers, parameters, and control flows
5️⃣ Ensure pipeline performance, monitoring, and cost optimization
6️⃣ Collaborate with data engineers, BI developers, and analytics teams and more…!

What does our Fabric Data Factory Training course contains?

The course is carefully curated with below module:
👉🏻Module 1: MSSQL & TSQL Queries
👉🏻Module 2: Azure Data Engineer
👉🏻Module 3: Power BI

Who can join this course?

Freshers aspiring for cloud data engineering roles
ETL developers transitioning to Fabric pipelines
SQL/DBA professionals looking to upskill on Fabric Data Factory
IT professionals interested in data orchestration and automation
Anyone passionate about modern cloud data pipelines

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 Fabric Data Factory 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
Weekly Mock Interviews
24/7 LIVE Server Access
Realtime Project FAQs
Course Completion Certificate
Placement Assistance
Job Support