Decathlon Switches to Polars to Optimize Data Pipelines and Infrastructure Costs
Migrating small-to-medium data workloads from Apache Spark to Polars yields major performance and cost improvements by enabling single-node execution and faster in-memory processing.
Use Polars, pandas, Matplotlib, and marimo to build interactive quasar redshift dashboards; use Faker for realistic test data; follow Python tooling and Django updates.
7 newer data science tools you should be using with Python
Several lesser-known Python data-wrangling tools, including ConnectorX and DuckDB, enable faster, parallelized database loading and in-process OLAP analytics beyond Pandas/Numpy.
The startup behind open source tool Polars raises $21M from Accel | TechCrunch
Polars raised €18 million to scale its Rust-based high-performance data processing platform, funding Polars Distributed and Polars Cloud for petabyte-scale workloads.
Episode #260: Harnessing the Power of Python Polars - The Real Python Podcast
Polars offers significant speed advantages, particularly in data projects when compared to pandas, making it an optimal choice for performance-centric applications.
How to Group Data Using Polars .group_by() Quiz - Real Python
This interactive quiz tests your understanding of Polars' .group_by() technique for data aggregation and provides a scoring system to evaluate your grasp of the concepts.
