Completed Associate Data Engineer in SQL

A more structured understanding of SQL

Completing the Associate Data Engineer in SQL learning path gave me a structured way to develop the database side of my skills. I came to it from textile engineering and merchandising, so it was useful to study how data is organised as well as how to query it.

What I worked through

• Joins and set operations. Combining related data while paying attention to keys and the number of rows each join produces.
• Database design. Understanding primary keys, foreign keys and the reasons for separating related entities.
• Data warehousing. Thinking about facts, dimensions and the difference between operational records and analytical models.
• Snowflake concepts. Extending my understanding beyond a local relational database.

Applying it to portfolio work

My Olist project uses SQL analysis alongside Power Query and Power BI. My ETL project takes a different direction, using PostgreSQL for cleaning, deduplication and audit logging, with pgAgent scheduling the workflow.

These projects make the learning concrete. A query can return a plausible answer even when a join has duplicated records. A transformation can work on one file but fail on a different date format. I want my project notes to show how I think about those problems, not just the final output.

The next part of the toolkit

I am continuing with the Data Engineer in Python track. My aim is to build on SQL with tools for working with APIs, files and workflows that involve several systems.