Polars Cloud Emerges: Run DataFrames Anywhere with Unmatched Performance!
Polars Cloud: High-performance DataFrame processing with flexible APIs, distributed computing, and seamless scalability for SQL-like efficiency in the cloud era.
"Top Python Libraries" Publication 400 Subscriptions 20% Discount Offer Link.
When I first started working with Polars, I realized that DataFrame is quite different compared to SQL and databases.
SQL databases can run in various environments, whether it's a small local application, a client-server setup, or even a large-scale OLAP data warehouse.
But what about DataFrame?
Different use cases require different APIs, and the performance is significantly worse compared to SQL. Locally, pandas are commonly used, while PySpark is the go-to for remote or distributed scenarios.
Pandas is indeed convenient to use, but it feels like it hasn't learned from decades of database experience! There's no query optimization, poor implementation of data types, many unnecessary materialization operations, and memory management is left to NumPy. These design choices result in poor scalability and inconsistent behavior.