Apache Arrow Data Framework Overview | Generated by AI
Apache Arrow is an open-source, language-agnostic software framework developed under the Apache Software Foundation. It provides a standardized columnar memory format for flat and nested data, designed to enable efficient in-memory analytics and fast data interchange across different programming languages and systems (such as Python, Java, R, C++, and more).
Key Features and Benefits
- Columnar Storage: Organizes data in columns rather than rows, which optimizes for analytical queries by reducing memory usage and improving CPU efficiency on modern hardware.
- Zero-Copy Reads: Allows seamless data sharing between processes and languages without serialization/deserialization overhead, speeding up workflows in big data tools like Apache Spark, Pandas, or Dremio.
- Multi-Language Support: Includes libraries and toolkits for over a dozen languages, making it a universal format for data processing pipelines.
- Extensions like Arrow Flight: A protocol for high-performance data transport over networks, further accelerating data movement.
It’s widely used in data engineering, machine learning, and analytics ecosystems to handle large-scale datasets more effectively than traditional row-based formats.
References
- Apache Arrow Official Site
- Apache Arrow GitHub Repository
- Apache Arrow on Wikipedia
- What Is Apache Arrow? (Dremio Guide)
- Introduction to Apache Arrow (The New Stack)