CF

Introduction to Data Engineering

57m 26s
English
Paid
Updated September 2026

Introduction to Data Engineering is a 11-lesson 57 minutes self-paced course by Zero To Mastery. Raw data is rarely usable on its own.

Course facts

Lessons
11
Duration
57 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Zero To Mastery
Price
Premium

Raw data is rarely usable on its own. It has to be cleaned, structured, and moved before anyone can build insights or AI systems on top of it. That work is what data engineering actually is, and this short course introduces the fundamentals behind it.

What you'll cover

  • Cleaning unstructured data: handling missing values, outliers, and inconsistencies
  • Processing and managing large datasets while keeping them accurate and accessible
  • Getting hands-on with SQL, Python, and pipelining tools used in the field
  • Understanding the full data lifecycle, from acquisition through transformation and analysis

It's built as an entry point, useful whether you're starting a data engineering career from scratch or you already work with data and want to formalize how you handle it.

Who teaches Introduction to Data Engineering? Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

What lessons are included in Introduction to Data Engineering?

This is a demo lesson (10:00 remaining)

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#1: Introduction
All Course Lessons (11)
#Lesson TitleDurationAccess
1
Introduction Demo
03:25
2
What Is Data?
06:43
3
What is a Data Engineer - Part 1
04:22
4
What is a Data Engineer - Part 2
05:37
5
What is a Data Engineer - Part 3
05:05
6
What is a Data Engineer - Part 4
03:23
7
Types of Databases
06:51
8
Optional: OLTP Databases
10:55
9
Hadoop, HDFS and MapReduce
04:23
10
Apache Spark and Apache Flink
02:08
11
Kafka and Stream Processing
04:34
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Frequently asked questions

What prerequisites are needed for this course?
This introductory course is designed for aspiring data engineers and does not require prior experience in data engineering. However, familiarity with basic programming concepts and a general understanding of data structures can be beneficial. The course will cover tools like SQL and Python, so having some prior exposure to these languages can be helpful.
What projects or exercises will I work on?
Throughout the course, you will engage in hands-on exercises that involve data cleaning and preparation, processing large datasets, and managing data with tools like SQL and Python. You'll explore real-world scenarios using Hadoop, HDFS, and MapReduce, as well as stream processing with Apache Spark and Apache Flink, to solidify your understanding of data engineering concepts.
Who is the target audience for this course?
The course is aimed at individuals aspiring to become data engineers, as well as professionals in related fields who wish to gain foundational knowledge in data engineering. It is suitable for those looking to understand how to transform raw data into actionable insights to drive business decisions and AI innovations.
How does the depth of this course compare to other data engineering courses?
As an introductory course, it focuses on foundational concepts and essential tools in data engineering. The course covers data cleaning, processing, and management techniques, and introduces powerful tools like SQL, Python, and Apache technologies. It is designed to provide a solid grounding for further study in more advanced data engineering topics.
What specific tools and platforms are taught in this course?
The course includes hands-on experience with essential tools used by professional data engineers, such as SQL and Python. Additionally, it introduces data processing frameworks like Hadoop, HDFS, and MapReduce, and addresses stream processing with tools like Apache Spark and Apache Flink, along with Kafka for stream processing.
What topics are not covered in this course?
This introductory course does not delve into advanced data engineering concepts such as deep machine learning integration, advanced data warehousing, or real-time data analytics. It focuses on foundational skills, providing the groundwork necessary to explore these advanced topics in future studies.
What is the time commitment required for this course?
The course consists of 11 lessons, covering a comprehensive range of foundational topics in data engineering. While the total runtime is not specified, students should allocate adequate time to engage with the material, complete hands-on exercises, and practice using tools like SQL, Python, and Apache technologies to fully grasp the concepts.