Elementary Data Engineering
Instructor:
Mohammad Fozouni
Learn the most common tools in the world of data engineering, various data formats, Python, and SQL in the shortest possible time.
Course content
1- What is Data Engineering?
In this section, we will try to point out the most important reasons for entering the world of data engineering and, by introducing several terms and examples from this field, fully familiarize the audience with data engineering. We will explore why in recent years we have been compelled to first prepare the data infrastructure before carrying out tasks related to artificial intelligence and data science, and only after completing this part move on to algorithms.
2- Familiarity with Operating Systems and Servers
One of the most important tools for any data engineer, before stepping into advanced technologies in this field, is familiarity with operating systems such as Windows and Linux. At the beginning of the course, we will try to familiarize the student with key concepts in this area. Please note that this entire course is prepared and published on the Windows operating system. Therefore, in this session related to operating systems, we will also discuss and review the PowerShell command line in some detail.
3- Familiarity with Various Data Files: CSV, JSON, AVRO
In the world of working with data, many extensions and formats are used for data storage. However, three of them are particularly important and practical, namely CSV, JSON, and AVRO. In this course, we will familiarize you with these three data storage formats by providing various examples. Two of them are what we call Human-Readable, meaning they can be read by humans, while the third is in Binary Format, meaning it is not readable by humans.
4- Familiarity with Databases (Relational and Non-Relational)
Familiarity with databases is considered one of the most essential pieces of knowledge for a data engineer. In recent years, PostgreSQL, as one of the most advanced open-source databases in the world, has gained a special place among business owners. Therefore, gaining familiarity and working professionally with this database is of great importance for data engineers. In this course, we will work with this database using a graphical tool. Additionally, we will discuss non-relational databases to some extent, so that you become familiar with the counterpart to a relational database.
5- Familiarity with Cloud Storage Space
In this section, we will become familiar with key concepts of cloud computing and the various services available in the Arvan Cloud panel. One of the most critical parts of this service is cloud storage space, and in this course, we will introduce you to this service through practical examples.
6- Python Programming Language
One of the most important programming languages in the world of working with data is Python. In this course, we will become familiar with the most important principles and libraries in the Python ecosystem that are widely used in data engineering, and we will perform practical work with them.
7- Fundamentals of APIs (Communication Between Programs and Systems)
One of the primary methods in the world of data engineering for retrieving and sharing data between multiple devices is working with APIs. In this course, we will first define this concept and explain its applications. Then, we will look at several practical examples.
8- SQL
After Python, the second and most important language for working with data is SQL. In this course, based on the PostgreSQL database, we will cover the most important and practical syntaxes in the world of SQL. After becoming familiar with Python and SQL, you will have acquired all the necessary fundamentals for working in the field of data engineering.
9- Final Project - Data Transfer and Air Pollution Analysis
In this project, using a Python script that simulates data generation for us, we will collect air pollution data from several different regions and aggregate them into a database. Then, we will analyze the pollution levels and attempt to visualize the pollution status over the past few hours by creating charts.
10- What to Do After Completing the Course?
In the final part of the course, we will have a session on mapping out the future path and prospects in the world of data engineering. This will include what to study and which courses to take. This session will undoubtedly help you take more confident steps if you wish to continue your journey in the world of data and data engineering.
Telegram Group
Asking your questions from instructor directly
Spot Player
To see videos online without downloading
Introductory Level
Give you a good roadmap for your future instantly
11 Hours
The most applicable and important DE concepts
Registration
Contact our support team to get the details about payment method.
What you'll learn
This course will equip you with the complete foundational skill set—from data ingestion (APIs, formats) and storage (Databases, Cloud) to processing (Python, SQL) and system management (OS, PowerShell)—required to begin working as a data engineer and to build a real-world data pipeline from start to finish.
Who is this course for?
This course is designed as a foundational or entry-point program for:
Aspiring Data Engineers: Individuals aiming to start a career in data engineering.
Data Analysts or Scientists: Professionals looking to understand the data infrastructure (“data plumbing”) that supports their work.
Software Developers/IT Professionals: Those wanting to transition into or understand the data ecosystem.
Motivated Beginners: Individuals with strong logical thinking and a commitment to learning, even if they have minimal prior coding experience.
Description
Launch your career in one of tech’s most in-demand fields. This comprehensive course provides the essential, hands-on foundation for becoming a Data Engineer. You’ll master the core tools of the trade: from Python and SQL for data manipulation, to PostgreSQL databases, cloud storage and APIs for data integration. Learn to work with key data formats like CSV, JSON, and Avro, manage systems via PowerShell, and architect a complete data pipeline from ingestion to insight in a capstone project simulating real-world air pollution analysis.
By the end, you’ll have the practical skills to build, manage, and deploy robust data infrastructure—the critical backbone of AI and data science. No prior data engineering experience is required, but basic computer literacy and logical thinking are recommended. Your journey to becoming a data engineer starts here.
Key Skills You’ll Gain:
- Python Programming for Data
- SQL & Database Management (PostgreSQL)
- Cloud Computing & Storage Fundamentals
- Building Data Pipelines with APIs
- Working with Core Data Formats (CSV, JSON, Avro)
- System Basics with Windows & PowerShell
Instructor
Mohammad Fozouni (Ph.D.). From 2014, I officially entered the teaching profession, and to this day, I have proudly educated thousands of students in all around the globe.