CF

Introduction to Data Engineering 2025

44m 26s
English
Paid

Welcome to your comprehensive introduction to Data Engineering, a foundational course designed to enhance your understanding of this pivotal field and the essential role of a Data Engineer within Data Science. You'll start with insights into my professional journey and experience, setting the stage for an engaging learning experience.

The Data Science Ecosystem

Key Roles in Data Science

Discover the integral professions within the Data Science landscape, including Data Scientists, Data Analysts, Machine Learning Engineers, and more. We'll explore the specific skills required, day-to-day tasks, and how these roles interconnect to drive data-driven success.

The Data Engineer's Profession and Skills

Understanding Data Engineers: Gain clarity on who Data Engineers are, what they accomplish, who they serve, and the crucial skills and tools they employ. Learn about their workflow, the architecture of data platforms, and toolsets utilized across various stages of data pipeline development. You will also get insights into salary expectations for Data Engineers.

Machine Learning Integration

The Machine Learning Lifecycle

Explore the standard processes of machine learning, focusing on model training and production deployment. We will identify the Data Engineer's role within this lifecycle, including their contributions to data handling and model management.

Project Dynamics in Data Science and Engineering

Understanding Project Context and Phases

Analyze the business context of Data Science and Engineering projects, delving into goal-setting responsibilities, project phases—emphasizing the MVP (Minimum Viable Product) phase—and the structuring of analytical processes.

About the Author: Andreas Kretz

Andreas Kretz thumbnail

Andreas Kretz is a German data engineer and one of the most widely followed independent voices on data engineering as a career discipline. He runs the Plumbers of Data Science brand and has been publishing tutorial material continuously since the field consolidated around the modern lake-house stack (Spark, Kafka, Snowflake, Databricks, Airflow).

His CourseFlix listing is the largest single-author catalog under this source — over thirty courses spanning data-pipeline construction, streaming architectures, the cloud-native data stack on AWS / Azure / GCP, the Python and Scala tooling that dominates the field, and the soft-skills / career side of breaking into data engineering. Material is paid and aimed at engineers transitioning into data work or already-working data engineers picking up specific tools.

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#1: Introduction
All Course Lessons (12)
#Lesson TitleDurationAccess
1
Introduction Demo
01:11
2
About My Journey as a Data Engineer
04:35
3
Data Science Jobs
06:00
4
Full Stack Data Scientists?
02:18
5
Science and Engineering
00:41
6
Who are Data Engineers
02:03
7
Data Platform & Tools
04:42
8
Engineering Tools in the Blueprint
04:30
9
Data Engineers and Machine Learning
04:26
10
ML Only a Small Part of Data Science
04:23
11
Phases of Data Science Projects
03:45
12
Jobs Within DS Project Phases
05:52
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Frequently asked questions

What prerequisites are needed for this course?
The course does not specify formal prerequisites. However, a basic understanding of data science concepts and familiarity with data handling tools would be beneficial. The content covers foundational aspects of data engineering and introduces key roles in data science, which suggests that some prior exposure to the field could help in grasping the material more effectively.
What projects or exercises will I work on during the course?
The course includes an exploration of the machine learning lifecycle and the role of data engineers within it. Although specific projects aren't detailed, lessons such as 'Data Platform & Tools' and 'Engineering Tools in the Blueprint' suggest practical engagement with data platform architecture and toolsets. These lessons likely involve exercises related to data pipeline development and management.
Who is the target audience for this course?
This course is designed for individuals looking to understand the role of a Data Engineer within the data science ecosystem. It is suitable for those interested in exploring the various professions within data science, including Data Scientists, Data Analysts, and Machine Learning Engineers, as well as those who want to learn about the tools and workflows specific to data engineering.
How does this course compare in depth and scope to other data engineering courses?
This course provides a foundational overview of the data engineering field, with insights into key roles, tools, and project phases within data science. It focuses on the Data Engineer's contributions to the data science ecosystem, particularly in the context of machine learning and project dynamics. While it offers comprehensive coverage of introductory topics, those seeking advanced technical skills may require additional specialized courses.
What specific tools or platforms will be covered in the course?
The course discusses various tools and platforms used by Data Engineers, particularly in the lesson 'Data Platform & Tools'. While specific tools are not named in the course overview, this lesson implies coverage of the architecture and toolsets that support data pipeline development and management in a data science context.
Are there any topics explicitly not covered in this course?
The course does not focus on in-depth machine learning model development, as it emphasizes that 'ML Only a Small Part of Data Science'. Instead, it concentrates on the role of Data Engineers in supporting the data science lifecycle, including data handling and model management, rather than the development of machine learning algorithms themselves.
How will this course benefit my career in data science?
By completing this course, you will gain a clear understanding of the Data Engineer's role within the data science ecosystem, including skills and tools crucial for data platform development. This foundational knowledge is valuable for anyone pursuing a career in data science, as it provides insights into how various roles interconnect and contribute to data-driven success, potentially opening up opportunities in data engineering or related fields.