🚀 Testing technologies for large-scale data analysis#
I was reading a very interesting benchmark that compares DuckDB, SQLite and Pandas working with over 1 million rows.
The focus: speed, memory usage, and efficiency in common analysis tasks (aggregations, filters, group-by).
🔍 Highlights#
- 🦆 DuckDB: fast, consistent, and very balanced in memory usage.
- 🐼 Pandas: sometimes the fastest, but can consume a lot of RAM.
- 🧱 SQLite: simple and stable, although the slowest of the group.
🧩 In short#
If you’re just getting started in data analysis:
- Pandas is like a super-flexible calculator inside Python. Great for exploring data.
- DuckDB works like an SQL engine on your laptop, optimized for fast queries without needing a server.
- SQLite is a lightweight database that stores data in a file and supports SQL queries, but it’s not as optimized for heavy analysis.
In summary:
👉 For fast local analysis, DuckDB is gaining ground.
👉 Pandas remains the standard for data manipulation in Python.
👉 SQLite is useful, but not the most efficient for large volumes.
More information at the link 👇
Also published on LinkedIn.
