Becoming a Better Data Engineer
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.
Watch Online
All Course Lessons (20)
| # | Lesson Title | Duration | Access |
|---|---|---|---|
| 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 |
Unlock unlimited learning
Get instant access to all 19 lessons in this course, plus thousands of other premium courses. One subscription, unlimited knowledge.
Learn more about subscriptionComments
0 commentsWant to join the conversation?
Sign in to commentSimilar courses
Streaming with Kafka & Spark
Complete linear algebra: theory and implementation
Analytics Engineering for Data Professionals
Data Structures and Algorithmic Trading: Machine Learning
