Becoming a Better Data Engineer

1h 46m 10s
English
Paid

Course description

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.

This is why we created the course "How to Become the Best Data Engineer." It will 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.

Read more about the course

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.

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# Title Duration
1 Introduction 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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