I learned SQL very early in my career. At the time, I didn’t understand why, and for the first month or so, it didn’t make sense to me. The syntax didn’t resemble any language I had seen before, and it employed concepts with which I was unfamiliar. This all made SQL seem scary, and oddly enough, SQL hasn’t changed much in the past 40 years, which makes it even more of an oddity. ...
Have you ever tried setting up a database server just to learn SQL? Docker containers, admin credentials… Forget all that. Let me show you how to go from zero to running SQL in under 5 minutes. Why are we using DuckDB to learn SQL? No setup - Just import and start using it Real SQL - PostgreSQL compatible syntax so what you learn transfers Fast enough - Handles millions of rows on your laptop Python-native - Works well with lists, DataFrames, CSV files It is perfect for learning without infrastructure headaches. ...
Last week, we got DuckDB running with three hardcoded rows. That got us started—but three rows? You can eyeball that. Let’s generate hundreds of realistic customers and build a dataset worth exploring. Python has the perfect tool: faker. It’s a library that generates realistic fake data—names, emails, addresses, dates—anything you’d find in a real database. Let’s use it to build a dataset we can explore for the rest of this series. ...
I still remember losing schoolwork and video game progress because I forgot to save. That sinking feeling when hours of work vanish because you were too caught up in the flow to pause and save. In our last post, we created a customer database and generated 500 rows of fake data. Our in-memory database has the same problem—when Python exits, all that data vanishes: import duckdb con = duckdb.connect(':memory:') con.execute("CREATE TABLE customers (id INT, name VARCHAR)") con.execute("INSERT INTO customers VALUES (1, 'Alice')") print(con.execute("SELECT * FROM customers").fetchdf()) # Script ends... and the data is gone forever A database is supposed to provide persistent storage, isn’t it? Let’s fix that with one small change. ...
Remember back when we started, I mentioned SQL was difficult because of how I was thinking? I was asking it to perform steps to return data. This didn’t work because SQL uses a declarative syntax that describes the final result. Until I realized this, SQL felt hard. Let’s explore this concept further. Working with lists and loops When you work with lists in Python, one of the first tools you reach for is the for loop. The for loop is great because it lets you take every item in the list and apply some logic to it, one at a time. It might look something like this. ...