I sleep better knowing my agents work through the night. Less work for me in the morning.

My podcast processor transcribes & analyzes conversations. I started on my laptop, needed a little database to collect podcast data & metadata, & booted up a DuckDB instance.

But then the data started to grow, & I wanted the podcast processor to run by itself. I changed two little letters, & the database moved to the cloud :

# Before : local only
conn = duckdb.connect('podcasts.db')

# After : cloud-native
conn = duckdb.connect('md:podcasts.db')

Now, in the small hours, 10 robots listen & summarize podcasts for me while I sleep.

As I collect more & more podcast information, my data has grown. I’m using a larger instance of MotherDuck.

Relative query time across cloud data warehouses

Source : ClickBench

Aside from ease of use, there are real price-performance advantages. MotherDuck systems are two to four times faster than a Snowflake 3XL & from a tenth to a hundredth of the price.

Cloud data warehouse cost comparison by instance

Source : ClickBench

As the amount of data expands & I process more technology podcasts every day, I’m sure I’ll need a data lake. At that point, I can migrate to DuckLake.

Small data becomes big data faster than you know it.

From Small Data to Big Data - The progression from local development to cloud-scale analytics

Two letters changed everything. In this era, when those letters aren’t AI it’s worth paying attention.