Arrow

Arrow is a cross-language development platform for in-memory data. It provides a standardized, language-independent columnar memory format designed to accelerate analytical operations and improve memory efficiency on modern computing hardware. By utilizing a schema-driven approach, the framework enables the efficient organization of both flat and nested data structures.

The project functions as an analytical data processing engine that facilitates high-performance computation directly on memory-resident datasets. It distinguishes itself through a zero-copy architecture, which allows multiple processes to access shared memory buffers simultaneously. This capability eliminates the performance overhead typically associated with data serialization, duplication, or transit between different system components.

Beyond its core memory format, the library serves as an interoperability layer for data ingestion and export. It supports integration with common file formats, ensuring compatibility across diverse analytical tools and external storage systems. The platform includes a suite of computational kernels designed to execute vectorized operations, enabling high-speed processing of large-scale information.

Features

Columnar Formats - Provides a language-independent standard for organizing flat or nested data in memory.

Data Analytics Engines - Functions as a high-performance engine for executing complex queries directly on memory-resident datasets.

Data Format Interoperability - Provides a standardized interface for reading and writing data across diverse file formats like Parquet, ORC, and CSV.

Memory Formats - Structures data into a language-independent columnar format to accelerate analytical operations and improve memory efficiency.

Columnar Data Processors - Organizes large datasets into memory-efficient columnar structures to accelerate analytical queries.

Language-Neutral Data Serialization - Provides a framework for moving large datasets between systems using shared memory to eliminate serialization overhead.

Memory Layouts - Organizes data in contiguous memory blocks to maximize CPU cache efficiency and enable vectorized processing.

Serialization Frameworks - Implements a high-performance framework for serializing and deserializing structured data with schema-driven efficiency.

Vectorized Execution Engines - Operates on batches of data using computational kernels to optimize CPU usage for analytical queries.

Language Interoperability - Standardizes data formats across different programming languages to ensure seamless communication.

In-Process Analytics - Executes complex data queries and processing tasks directly on memory-resident datasets.

Shared Memory Transports - Provides zero-copy communication mechanisms for efficient data access across multiple processes.

Language-Agnostic Runtimes - Provides a standardized memory format that allows different programming languages to read and write data without translation overhead.

Vectorized Array Operations - Performs mathematical operations on entire arrays of data to leverage modern processor instruction sets.

Zero-Copy Mechanisms - Enables multiple processes to access shared memory buffers simultaneously without serialization or duplication overhead.

Data Storage Systems - Provides in-memory columnar data representation.

Data Engineering - Columnar format for fast data interchange.

Developer Tools - Multi-language toolbox for accelerated data interchange.

Data Import and Export - Ingests and exports information across standard file types like CSV, ORC, and Parquet.

Data Serialization Formats - Defines structured metadata for nested and flat data types to facilitate efficient reading from various file formats.

Schema Metadata Utilities - Manages structured metadata to define data layouts for efficient reading across different systems.

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Features

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