September 20, 2023

Beginner Friendly Data Engineering Courses in 2023

Some excellent courses tailored for beginners who aspire to learn the fundamentals of data engineering and kickstart their career in this exciting field.

Data engineering is a crucial aspect of the data science and analytics domain, focusing on the design, construction, and maintenance of data pipelines, databases, and data warehouses.

In this blog, we will explore some excellent courses tailored for beginners who aspire to learn the fundamentals of data engineering and kickstart their career in this exciting field.

5 Data Engineering Courses to consider

Data Engineering Foundations Specialization by IBM on Coursera

Level: Beginner | Duration: Approx 2 months (10 hours/week) | Fee: Free to audit, upgrade for certificates and financial aid also available | Link to the courses:Here.

What you’ll learn: Data Engineering Ecosystem and Lifecycle, Python Programming Basics, Relational Database fundamentals and working with MySQL, PostgreSQL & IBM Db2, SQL

IBM Data Engineering

About the course: This Specialization consists of 5 self-paced online courses that encompass the essential skills needed for data engineering, covering topics such as the data engineering ecosystem and lifecycle, Python, SQL, and Relational Databases.

By engaging with instructional videos and hands-on exercises using authentic tools and real-world databases, you will acquire these fundamental data engineering prerequisites. As a result, individuals will develop a comprehensive understanding of data engineering, attain practical skills applicable to a data-oriented profession, and lay the groundwork for a successful data engineering career.

IBM Data Engineering Professional Certificate on Coursera

Level: Beginner | Duration: 5 Months (10 hours/week) | Fee: Free to audit, upgrade for certificates and financial aid also available | Link to the courses: Here.

What you’ll learn: Data Science, ETL & Data Pipelines, Relational Database Management Systems (RDBMS), NoSQL and Big Data, Python Programming, Data Analysis, Database (DBMS), Apache Spark, SQL

About the course: This professional certification consists of 13 courses that will teach you data engineering from scratch and if you want a career in Data engineering then this program will teach you the foundational data engineering skills employers are seeking for entry level data engineering roles, including Python, one of the most widely used programming languages. You’ll also master SQL, RDBMS, ETL, Data Warehousing, NoSQL, Big Data, and Spark with hands-on labs and projects.

Data Engineer Career Path - Microsoft Learn Official Collection

Level: Intermediate | Duration: Approx 30-35 hours | Fee: Free, no certificates | Link to the course: Here.

What you’ll learn: Azure, Azure Synapse, Apache spark pools, work with Data warehouses using azure synapse, transfer and transform data using azure synapse analytics pipelines, work with Azure Data bricks

Microsoft Course for Data Engineering

About the course: Microsoft provides one of the best free learning materials about Data science and AI. This Data engineer career path consists of 9 courses and prepares you for Azure Data Engineer Associate Certifications exam DP-203. These courses have modules that will help you build  skills and advance your career in Data Engineering.

The Data Engineer Career Path is designed to help you learn how to design and implement the management, monitoring, security, and privacy of data using the latest technologies and tools. You can find more information about this career path on the Microsoft Learn website.

Big Data and Hadoop Essentials- Udemy

Level: Beginner | Duration: Approx 43 min | Fee: Free | Link to the course: Here.

What you’ll learn: understanding Big Data

Udemy Big Data & Hadoop Course Screenshot

About the course: This short course is for those who are curious to know what Big Data is all about. If you are looking to understand how Big Data impact large and small business then this course is for you.

The course covers the following topics:

1. Unraveling Big Data problems through easy-to-understand examples.

2. Tracing the origins and evolution of Hadoop, from its early days before it was even called "Hadoop."

3. Discovering the magic of Hadoop that makes it uniquely powerful.

4. Clarifying the distinction between Data Science and Data Engineering, a common source of confusion when choosing a career path or understanding job roles.

5. Demystifying Hadoop vendors like Cloudera, MapR, and Hortonworks by learning more about them.

By the end of the course, you'll have a solid foundation in tackling Big Data challenges and utilizing Hadoop to address them effectively.

IBM: Data Engineering Basics for Everyone on edx

Level: Beginner | Duration: 4 weeks (9-10 hours/week) | Fee: Free | Link to the course: Here.

What you’ll learn: Hadoop, HDFS, Hive, and Spark, ETL, ELT, and Data Pipelines, Data Warehouses, Data Marts, and Data Lakes, RDBMS, NoSQL, Data Wrangling and querying etc.

About the course: This course is designed to introduce you to the fundamental concepts of data engineering, its ecosystem, lifecycle, processes, and essential tools. The Data Engineering Ecosystem comprises various components like data repositories, integration platforms, data pipelines, languages, and BI/reporting tools. Data pipelines acquire data from diverse sources, while repositories store and process it. Integration platforms create a unified view for secure access by data consumers who use BI and analytical tools for valuable insights.

IBM Data Engineering Basics

Through practical labs, you'll provision a data store on IBM Cloud, load data, and gain hands-on experience in data processing.


These carefully curated programs offer a comprehensive understanding of essential concepts, tools, and technologies required in the data engineering ecosystem. From mastering programming languages like Python and SQL to exploring data warehouses, ETL processes, and big data technologies, these courses will equip you with the knowledge and practical skills needed to succeed as a data engineer.

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