BLOG
← Back to Glossary
PyArrow
PyArrow is a Python library that provides a high-performance interface for working with columnar data structures, particularly those defined by the Apache…
PyArrow is a Python library that provides a high-performance interface for working with columnar data structures, particularly those defined by the Apache Arrow format. It enables efficient data exchange between various data processing systems and languages without the need for serialization or deserialization. PyArrow offers tools for reading and writing common file formats like Parquet and Feather, as well as integration with popular data analysis libraries such as pandas and NumPy. Data engineers and analysts can leverage PyArrow to significantly speed up data processing tasks, especially when dealing with large datasets. Its zero-copy reads and interoperability with DuckDB make it an excellent choice for building data pipelines and performing analytics on structured data.
Related terms
Feather format →
Feather is a fast, lightweight binary file format for storing columnar data frames on disk, based on the Apache Arrow columnar memory format, commonly used for quick interchange between pandas, R, and other Arrow-compatible tools.
Apache Arrow →Apache Arrow is an open-source, cross-language development platform for in-memory columnar data.
pandas →pandas is a powerful, open-source data manipulation and analysis library for Python.
Polars →Polars is a high-performance data manipulation library written in Rust, designed to handle large datasets efficiently.
Data serialization →Data serialization is the process of converting in-memory data structures into a format that can be stored on disk or transmitted over a network, and later reconstructed through deserialization.
PyIceberg →PyIceberg is the official Python implementation of Apache Iceberg — a pure-Python library for reading, writing, and managing Iceberg tables and catalogs without requiring Spark or a JVM.
