Data Engineering Lessons

[boitumelodikoko]

Getting into the data space can feel overwhelming, with so many tools, terms, and technologies. But after years in

Expect failure. Design for it.
Jobs will fail. The data will be late. Build systems that can recover gracefully, and continually monitor your pipelines.

Think like an engineer.
Use Git—Automate where possible. Learn the basics of DevOps (CI/CD, testing, infrastructure as code). You'll stand out because many skip this.

Reproducibility builds trust.
If someone can't trace how you got a result, it's not reliable. Always aim for results that are transparent and repeatable.

Understand the problem, not just the data.
Tools change, but solving real-world problems doesn't. Stay close to the "why" behind the work — it's what separates good from great.

Whether you're just starting or mentoring others, what do you think belongs on this list?

[Gecofer]

Hi @boitumelodikoko 

A few more principles I always share with people entering the data space:

Observability is non-negotiable.

• If you can’t see what your pipelines are doing, you can’t fix what breaks.
• Good logging, metrics, and alerts save countless hours and prevent silent failures.

Document as you build, not afterward.

• Clear explanations, consistent naming, and simple diagrams make your work usable for others and for your future self.

Keep pipelines modular and predictable.

• Small, focused components are easier to test, reuse, and debug.
• Monolithic notebooks filled with hidden logic are where most long-term problems begin.

Treat data quality as a first-class citizen.

• Constraints, schema checks, and validation rules prevent bad data from cascading into bigger issues downstream.