"Most data sits in a database somewhere, but computation typically happens outside of it. Getting data to and from the database for actual work can be a slowdown. ConnectorX loads data from databases into many common data-wrangling tools in Python, and it keeps things fast by minimizing the work required. Most of the data loading can be done in just a couple of lines of Python code and an SQL query."
"Like Polars (which I'll discuss shortly), ConnectorX uses a Rust library at its core. This allows for optimizations like being able to load from a data source in parallel with partitioning. Data in PostgreSQL, for instance, can be loaded this way by specifying a partition column. Aside from PostgreSQL, ConnectorX also supports reading from MySQL/MariaDB, SQLite, Amazon Redshift, Microsoft SQL Server and Azure SQL, and Oracle."
Python's data science ecosystem contains powerful, lesser-known tools that improve data wrangling performance and workflow efficiency. ConnectorX transfers data from databases into Python dataframes with minimal overhead, using a Rust core to enable parallel, partitioned reads. ConnectorX supports PostgreSQL, MySQL/MariaDB, SQLite, Amazon Redshift, Microsoft SQL Server, Azure SQL, and Oracle, and can target Pandas, PyArrow, Modin, Dask, or Polars outputs. DuckDB offers an in-process, OLAP-oriented relational engine with columnar storage that delivers analytics-focused performance while maintaining SQLite-like simplicity. Tools like Polars and ConnectorX leverage Rust for speed and concurrency in modern data pipelines.
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