CF

Becoming a Better Data Engineer

1h 46m 10s
English
Paid

Becoming a Better Data Engineer is a 20-lesson 1 hour 46 minutes self-paced course by Andreas Kretz. Data engineering is not just about moving information from one place to another.

Course facts

Lessons
20
Duration
1 hour 46 minutes
Level
All levels
Language
English
Updated
Instructor
Andreas Kretz
Price
Premium

Data engineering is not just about moving information from one place to another. It is about creating reliable, scalable, and efficient systems that transform raw data into valuable insights. However, in practice, many engineers face chaotic tasks, switching from one problem to another—without a clear strategy and structure.

Course Overview

We created the course "How to Become the Best Data Engineer" to help you tackle real challenges in Data Engineering—from planning and designing systems to implementation and support. You will master proven approaches that allow you to prevent errors before they occur and build functional pipelines, rather than temporary solutions.

By the end of the course, you will apply a structured approach to any project, work more efficiently, and solve problems like a true professional—whether you are starting from scratch or looking to move to the next level.

What to Expect in the Course

Introduction to Data Engineering and Project Stages

You will understand the complete data path—from sources to storage and end users. Learn about the four key stages of a project: Planning, Design, Implementation, and Support, and why each is critical for success.

Avoiding Common Mistakes in Projects

Many engineers suffer from unclear requirements, unrealistic deadlines, and communication issues. You will learn to set measurable goals (KPIs), manage stakeholder expectations, and prevent scope creep.

Building and Optimizing Pipelines

A good pipeline is not just functional but also scalable, resilient, and maintainable. You will study data integration design, error processing, performance improvement, and reliability assurance on a long-term basis.

Support: Monitoring, Debugging, and Scaling

You will learn how to track failures, quickly find and fix errors, and scale infrastructure without extra costs.

Who teaches Becoming a Better Data Engineer? 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.

What lessons are included in Becoming a Better Data Engineer?

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#1: Introduction
All Course Lessons (20)
#Lesson TitleDurationAccess
1
Introduction Demo
01:33
2
Data Engineers & What We Do
06:33
3
Phases Of Data Projects
05:35
4
General Areas To Improve
06:35
5
Understanding The Requirements Better
03:20
6
Not Forgetting To Analyze The Status Quo
07:19
7
Setting Good KPIs
06:06
8
Improving Estimation Of Implementation Efforts
04:58
9
Designing Better Platforms
06:10
10
Calculating Costs By Leveraging Pricing Models
06:20
11
Running Good Benchmarks To Make Right Platform Choices
06:50
12
Define Better Work Packages
04:00
13
Analyze & Avert Risks Like A Pro
06:53
14
Write Better Tests
06:05
15
Create Documentation That Actually Helps People
06:54
16
Great Monitoring & Alarming
06:08
17
How To Handle Bug Fixing Like A Pro
05:37
18
Create A Documentation For Ops
04:20
19
Continuous Improvement
03:35
20
Conclusion
01:19
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What courses are similar to Becoming a Better Data Engineer?

Frequently asked questions

What prerequisites are needed for this data engineering course?
The course does not specify any formal prerequisites. However, a basic understanding of data systems and engineering principles may be beneficial. Knowledge in areas such as project management, system design, and data pipeline construction could help you grasp the content more effectively, as the course dives into complex topics like setting KPIs, designing platforms, and cost estimation.
What will students build during the course?
Students will focus on building efficient data pipelines and systems. The course covers designing better platforms and running benchmarks to choose the right platforms. Additionally, students will learn to set up effective monitoring and alarming systems, write better tests, and create helpful documentation, all of which culminate in constructing reliable and scalable data engineering solutions.
Who is the target audience for this course?
The course is ideal for data engineers at various levels who want to improve their system-building skills. Whether you are starting from scratch or looking to advance to the next level, the structured approach to planning, designing, implementing, and supporting data systems will help you tackle real-world challenges in data engineering.
How does this course differ from other data engineering courses?
Unlike many courses that focus solely on technical skills, this course emphasizes a structured approach to data engineering projects. It covers the full data project lifecycle, including planning, design, implementation, and support. The course also addresses common pitfalls such as unclear requirements and unrealistic deadlines, teaching students how to set measurable goals and manage stakeholder expectations.
What specific tools or platforms will be highlighted in the course?
While the course primarily focuses on methodologies rather than specific tools, it does cover essential practices such as running benchmarks to make informed platform choices and leveraging pricing models for cost estimation. This approach helps students make strategic decisions about which tools and platforms to use in their projects.
What topics are not covered in this course?
This course does not focus on specific data engineering tools or technologies. Instead, it emphasizes the overall strategy and structure of data engineering projects. Therefore, students looking for in-depth tutorials on specific software or coding languages may need to seek additional resources.
How much time should I expect to commit to this course?
The course consists of 20 lessons. Although the exact runtime is not specified, students should prepare to dedicate time to thoroughly understand the project stages, methods for avoiding common project mistakes, and techniques for building and optimizing pipelines. A commitment to actively engaging with exercises and applying the concepts to real-world scenarios will enhance the learning experience.