I often receive questions from aspiring data engineers. Some are fresh grads, others are switching from software or analytics roles. And a same question appears so many times:
What tool should I learn in depth?
I understand why people keep having such questions. The tech world moves extremely fast. Every few months, there is a new framework, a new orchestration tool, a shiny feature in a cloud, or some articles filled with buzzwords that make you feel like you are already behind. The pressure to keep up is real. But here is something I have learned over the years, and I want to say it clearly: focus on learning the basics in depth. Tools will change, 0 and 1 will not.
Years ago, everyone was talking about HDFS, MapReduce, Pig, Hive. Fast-forward a few years, Spark took over. Then cloud-native pipelines. Now we have got real-time streaming, feature stores, vector databases, and AI-generated pipelines. If I had spent all my time chasing tools, I would be exhausted. Always playing catch-up — and probably still behind.
Instead, I focused on understanding how data actually works; how it moves; how it is stored and transformed; how to model it for clarity, flexibility, and performance; how SQL engines work under the hood; how replication and partitioning affects performance, how to make a data schema clean and extensible. Those things have not changed. These lessons apply whether you are working with BigQuery or Snowflake, self-hosted DBT or Azure Data Factory on the cloud, or something that does not even exist yet.
Data Engineering is developing in a direction where engineers write less and less code. Unlike several years ago, you now have a lot of tools publicly available. For a new project, you are provided with a comprehensive toolbox. Your primary task then becomes picking the right tools and making them work seamlessly together. By writing less code, it makes the engineers harder to understand the technology behind the scene. I began my technology journey by writing a lot of code in Pascal - my favorite programming language. I still write some nowadays in my free time. My coding experience helps me a lot. When I work with a tool today, I can often imagine the actual code running on the machine.
Yes, you are not required to be able to write the tools from scratch. But, you are required to understand the core technology behind the tools. Because even if you are just picking items from your toolbox, you still need to pick the right tools. And picking the right tools for the right jobs is still a highly advanced skill in our industry today.
Here is another undeniable truth we can not ignore: AI is becoming incredibly good at repeating what it has learned, and it is getting more and more involved in our daily work. Honestly, I use it a lot to help me develop. This fact actually highlights the advantages of understanding the basics. If AI is not good enough, people need you because you understand the underlying concepts and can operate the AI reliably. If the AI is already good enough, why do they still need you? Because you are better than AI at understanding the things it does not know – the true “why” behind the “what”.
When you understand the basics, tools become just syntax. You can pick up new ones quickly. You can even build your own if you want to. Investors care about one thing. It is the value you created, not the tools you used. They want to know: Did you help the business make better decisions? Did you save money? Did you unlock insights faster? Those results do not come from stacking the flashiest tools, but from engineers who know what matters and make the right decisions. That is what turns you from someone who follows instructions into someone who builds solutions.
And in this field, that is the difference between being useful and being indispensable.