Week 11/34: ETL and ELT Processes for Data Engineering Interviews #2
Understanding ETL and ELT best practices with a comprehensive case study with dlt and dbt
In our previous post, we explored the fundamentals of ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform). We compared their advantages and disadvantages, walked through real interview scenarios, and even presented a case study demonstrating how they can be implemented in a real technical assessment.
In this follow-up post, we’ll take a deeper dive into;
Best practices for both ETL and ELT,
Popular tooling options,
Building an end-to-end ELT pipeline using dlt , dbt and PostgreSQL.
All the codes for this pipeline are available and can be accessed from: [GitHub]1
Whether you’re preparing for a data engineering interview or looking to refine your existing data platform, this post can help you improve your data pipelines for performance, reliability, and scalability.
For the previous posts of this series, check here: [Data Engineering Interview Preparation Series]2
ETL and ELT Best Practices
“Murphy’s Law: Anything that can go wrong, will go wrong.”
This famous maxim is especially relevant in data engineering, where a single overlooked detail can cascade into significant pipeline failures. Nonetheless, not every recommendation or technique discussed here will be critical to every scenario. Data engineers should always consider requirements, business objectives, and constraints before deciding which best practices to adopt and how to prioritise them. By tailoring your approach to these specific demands, you can ensure your data pipelines remain effective and efficient.