Tutorials > Hands-On Data Engineering & Data Analysis with Azure Cloud

Hands-On Data Engineering & Data Analysis with Azure Cloud

Date: 1 September 2026 | Posted by: AD-TEAM

Welcome to
Hands-On Data Engineering & Data Analysis with Azure Cloud
.
Data is at the center of modern businesses, but collecting data is only the beginning. Organizations need professionals who can
store, process, transform, analyze, and move data efficiently
.
This course is designed to help
beginner and intermediate learners
build practical skills in
Data Analysis, SQL, Python, Cloud Computing, and Azure Data Engineering
through hands-on learning.
We start from the fundamentals, so you don't need advanced Data Engineering or Azure knowledge to begin.
Start with Data Fundamentals
Before working with tools, you will build a strong understanding of important concepts such as:

Data, Databases, and DBMS

Data Analysis

Data Engineering

Data Lifecycle

Modern Data Platforms

ETL vs ELT

Structured and Semi-Structured Data

Cloud Computing
These concepts will help you understand not only
how
to use data technologies, but also
why and where
they are used.
Learn SQL with Hands-On Practice
Next, you will learn SQL and relational database fundamentals.
You will work with SQL to:

Create databases and tables

Understand SQL data types

Insert single and multiple records

Retrieve data using SELECT

Rename result columns using aliases

Filter data using WHERE

Work with comparison operators

Combine conditions using AND, OR, and NOT

Filter using IN, LIKE, BETWEEN, and IS NULL

Sort data using ORDER BY

Use aggregate functions

Summarize data using GROUP BY

Filter aggregated results using HAVING
But this course goes beyond simply watching SQL demonstrations.
You will get
hands-on coding exercises
where you can write SQL yourself and test your understanding.
The course also includes
interactive role-play activities
designed to help you think like a Data Analyst and Data Engineer while solving realistic business requirements.
Python for Data Analysis
You will then explore
Python and Pandas for Data Analysis
.
You will learn how to:

Load and inspect datasets

Understand rows, columns, and dataset structure

Identify missing values

Handle NULL values

Use median and other calculations

Handle missing values while considering categories

Perform basic data calculations

Work with string functions

Clean and prepare data for analysis
This provides practical exposure to how Python can be used to explore and prepare real-world datasets.
Move to Microsoft Azure Cloud
Once the fundamentals are clear, we take our data engineering journey to the cloud.
You will learn how to work with important Azure data services including:

Azure SQL Database

Azure Data Lake Storage (ADLS)

Azure Data Factory (ADF)
You will create Azure resources and connect to Azure SQL using tools such as SQL Server Management Studio and Azure's query tools.
Build Azure Data Factory Pipelines
A major part of this course focuses on hands-on Data Engineering using
Azure Data Factory
.
You will learn how to:

Create Azure Data Factory

Understand the ADF interface

Create Azure Data Lake Storage

Create Linked Services and Datasets

Connect source and destination systems

Build data pipelines

Copy data between systems

Execute and validate pipelines

Monitor pipeline executions

Create triggers to automate pipeline executions

Connect on-premises data using Self-hosted Integration Runtime

Move data from on-premises systems to Azure

Load multiple files

Build dynamic and reusable pipelines
Build Metadata-Driven Data Pipelines
Instead of creating a separate pipeline for every table or dataset, you will learn how to design a
metadata-driven architecture
.
You will see how metadata and dynamic configurations can help create more reusable and scalable data pipelines.
Implement Incremental Data Loading
Finally, you will work with one of the most important concepts in practical Data Engineering:
Incremental Loading
.
You will learn how to:

Understand full load vs incremental load

Work with watermark values

Identify new or modified records

Dynamically retrieve watermark values

Load only required incremental data

Update watermark values using stored procedures

Validate incremental loads using different sets of data
Instead of reloading an entire dataset every time, you will understand how to design pipelines that process
only new or changed data
.
Hands-On Learning Approach
The goal of this course is not just to introduce tools.
We follow a practical learning approach:
Understand the Concept → Practice It → Build with It
By the end of the course, you will have a much clearer understanding of how data moves from source systems through processing and storage to become useful information for analytics—and how modern Azure Data Engineering solutions can be built to support that journey.
If you're ready to build practical skills in
Data Analysis and Azure Data Engineering
, let's get started.

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