Azure Data Engineer -

Introduction

Welcome to the Azure Data Engineer Tutorial by RajeshGowd.com — your destination for real-time, hands-on learning.

What exactly is data engineering, and what does a data engineer do?

In simple terms, data engineering is about building the systems that move, prepare, and store data so it can be used for reporting, dashboards, predictions, and business insights.

As data engineers, our main responsibility is to collect data from various sources — this could be websites, mobile apps, databases, third-party APIs, or even manual inputs from different departments.

Once we gather the raw data, we clean, transform, and organize it so that it's accurate, consistent, and ready for analysis. This includes tasks like removing duplicates, fixing errors, standardizing formats, and combining data from multiple systems.

After transforming the data, we store it in a centralized location — such as a data warehouse or data lake — using cloud platforms like Azure.

From there, Business Intelligence (BI) tools or data analysts take over. They use tools like Power BI, Tableau, or Excel to create dashboards and reports that visualize the data and help business teams make decisions.

These dashboards provide insights such as:

Are we making a profit or a loss?
Which products are performing the best?
Which regions or services need improvement?
Where should we invest or cut costs?

In short, data engineers build the pipelines and platforms that turn raw data into valuable insights, enabling businesses to make informed, data-driven decisions.

In this tutorial, you’ll learn the following cloud basics, languages, and tools like SQL, Python, Hadoop, and Spark, and how to implement them using Microsoft Azure

Cloud Basics

In this tutorial, you’ll learn the following cloud basics and how to implement them using Microsoft Azure.

What is a Cloud?

Cloud computing is the on-demand delivery of computing services (such as servers, storage, databases, networking, software, and analytics) over the internet. It allows users to access and use resources without owning or managing physical infrastructure.

What is Big Data?

Big data is a collection of huge data from various sources, and it requires specialized tools and technologies to process and analyze the data.

Volume: Large amount of data (59 ZB now, 175 ZB predicted in 2025).
Variety: Structured, semi-structured, and unstructured data formats.
Velocity: High-speed data generation and processing performance.

Cloud Service Categories

There are 3 main types of cloud services:

IaaS (Infrastructure as a Service): Provides virtualized computing resources over the internet.
Examples: CPU, RAM, storage, virtual machines (VMs)
PaaS (Platform as a Service): Offers hardware and software tools for application development.
Examples: SQL Server, operating systems, development frameworks
SaaS (Software as a Service): Delivers software applications via subscription over the internet.
Examples: Office 365, Google Workspace, Salesforce

Languages and Tools

To become proficient in data engineering, it’s essential to understand certain programming languages and tools used in the industry. Here’s a breakdown of some key ones:

SQL

SQL (Structured Query Language) is the foundational language used for querying and managing relational databases. It allows data engineers to extract, update, insert, and delete data in databases. SQL is essential for any data engineering project, as it helps manipulate and retrieve data from structured sources.

Use Case:Extracting data from a database, transforming it using SQL logic, and loading it into another system (ETL processes).

Learn more here: SQL Tutorial

Python

Python is one of the most popular programming languages in data engineering due to its simplicity, flexibility, and rich ecosystem of libraries. It’s used for automating tasks, writing transformation scripts, working with APIs, connecting to databases, and processing large datasets (especially with tools like Pandas and PySpark).

Use Case: Writing custom data transformation logic, building ETL pipelines in Databricks, or automating workflows.

Learn more here: Python For Data Engineering

Hadoop and Spark

Both Hadoop and Spark are big data frameworks used to process large volumes of data in distributed environments.

Hadoop

Hadoop is an open-source framework used for storing and processing large datasets across many computers. It uses a distributed file system (HDFS) to store data and a processing model (MapReduce) to analyze it. It’s ideal for batch processing and storing unstructured or semi-structured data in large-scale environments.

Use Case:Batch processing of logs or archived data over multiple nodes.

Learn more here: Hadoop Basics

Spark

Spark is a fast, in-memory data processing engine that builds on top of Hadoop. Unlike Hadoop, Spark allows for real-time data processing, enabling much faster analysis. It’s commonly used in big data applications for tasks like machine learning, real-time stream processing, and graph processing.

Use Case:Running distributed data transformations or ML workloads in Azure Databricks.

Learn more here: Spark Tutorial

Azure Services Covered

In this course, we’ll focus on how to use Microsoft Azure — one of the leading cloud platforms — to build and manage data engineering solutions. Here’s a quick overview of the key Azure services you’ll be working with:

To use any Azure services, you need an Azure subscription. It acts like your cloud account where you manage services, billing, and access.

Microsoft offers a free trial that includes:

₹14,500 (or $200) credit for 30 days
12 months of free access to popular services (like Azure SQL, Blob Storage)
Always-free services with limited usage (e.g., Event Grid, Functions)

Limitations:

Databricks is not part of the always-free tier
Services pause after the free credit or time limit ends unless you upgrade

Tip: Use free-tier services first to practice common data engineering tasks without extra cost.

Azure Data Factory (ADF)

Azure Data Factory is a cloud-based data integration service that allows you to move, transform, and orchestrate data across various systems. It enables the creation of automated data workflows between cloud and on-premises sources.

Use Case: Automating ETL (Extract, Transform, Load) workflows for large-scale data movement.

Learn more here: Azure Data Factory

Azure Databricks (ADB)

Azure Databricks is an Apache Spark–based analytics platform optimized for Azure. It provides a collaborative environment for big data analytics and machine learning. Data engineers use it to process large datasets using Python, R, or Scala.

Use Case: Running machine learning models and processing big data in real time.

Learn more here: Azure Databricks

Azure Storage

Azure Storage offers scalable cloud storage for various data types, including blobs, files, queues, and tables. It supports storing unstructured, semi-structured, and structured data.

Use Case: Storing large datasets like logs, backups, and images.

Learn more here: Azure Storage

Azure Key Vault

Azure Key Vault is a secure cloud service for storing and managing sensitive information like API keys, passwords, and certificates.

Use Case: Protecting sensitive data and controlling access to secrets within data pipelines.

Learn more here: Azure Key Vault

Azure Logic Apps

Azure Logic Apps enables you to automate workflows and integrate services, both within Azure and with external platforms like Microsoft 365, Dropbox, or Google services — all without writing complex code.

Use Case: Automating data integration and triggering workflows across multiple services.

Learn more here: Azure Logic Apps

Azure Active Directory (Azure AD)

Azure AD is Microsoft’s cloud-based identity and access management service. It’s used to authenticate users and secure access to applications and resources.

Use Case: Managing user identities and access permissions securely in the cloud.

Learn more here: Azure Active Directory

Azure App Registration

Azure App Registration allows you to register applications with Azure AD so they can securely access Azure services using APIs and OAuth2.

Use Case: Enabling secure, programmatic access to Azure services through your apps.

Learn more here: Azure App Registration

Azure SQL Database

Azure SQL Database is a fully managed relational database service offering high availability, automatic scaling, and built-in security.

Use Case: Hosting transactional SQL-based applications in the cloud.

Learn more here: Azure SQL Database

Azure Synapse Analytics

Azure Synapse Analytics (formerly SQL Data Warehouse) is a powerful analytics platform that combines big data and data warehousing. It supports querying data using both SQL and Spark.

Use Case: Analyzing massive datasets for reporting, dashboards, and business intelligence.

Learn more here: Azure Synapse Analytics

Azure Event Hubs

Azure Event Hubs is a big data streaming platform and event ingestion service. It can receive and process millions of events per second in real time from various sources such as applications, IoT devices, logs, or websites.

It’s often used at the entry point of real-time data pipelines — capturing continuous streams of data, which are then passed to processing engines like Azure Databricks, Stream Analytics, or Azure Functions.

Use Case:Collecting and ingesting real-time data such as user clicks from a website, telemetry from IoT devices, or log data from applications, and forwarding it for further processing or storage.