As a beginner in data engineering, it’s easy to feel overwhelmed by the sheer amount of information out there. With new technologies and tools emerging every day, it’s hard to know where to start.
I remember when the path to becoming a data engineer was clear: learn SQL, Python, and a tool like Airflow, build a data pipeline, and visualize the data with a fancy chart. That was it. Nowadays, the landscape is much more complex.
With so many new tools and technologies vying for our attention, it’s hard to focus on what truly matters. Do I need to learn about data quality frameworks like Great Expectations or Soda? Should I dive into distributed compute with Spark or BigQuery? And what about data lake tech like Iceberg and Delta?
Not to mention the bonus materials that seem to be popping up everywhere. Should I learn about vector database tech like Qdrant or Pinecone? Or maybe dive into retrieval augmented generation (RAG) and experimentation frameworks?
The truth is, you don’t need to learn everything. But you do need to learn the fundamentals. Here’s my take on the ideal beginner learning path for data engineering in 2025:
Start with the Basics
- Learn SQL and Python. These are the building blocks of data engineering.
- Get familiar with orchestration tools like Airflow. This will help you understand how to manage and schedule your data pipelines.Focus on Data Quality
- Learn about data quality frameworks like Great Expectations or Soda. This will help you ensure that your data is clean and reliable.Distributed Compute and Data Lakes
- Learn about distributed compute technologies like Spark or BigQuery. This will help you process large datasets efficiently.
- Familiarize yourself with data lake tech like Iceberg and Delta. This will help you store and manage your data effectively.Bonus Materials
- If you're interested in AI, learn about vector database tech like Qdrant or Pinecone. This will help you build more efficient AI models.
- If you're interested in data science, learn about experimentation and analytical frameworks. This will help you build more effective data models.The key is to focus on the fundamentals first. Don’t try to learn everything at once. Start with the basics, and then gradually build your skills from there. With persistence and dedication, you can become a successful data engineer in 2025.
*Further reading: Data Engineering Roadmap*