Sql

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. ...

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, and anything else 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 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. ...