Convert Parquet to Arrow

Parquet is a columnar storage file format designed for efficiency with big data processing frameworks like Apache Hadoop and Spark.

Technical Details

Parquet organizes data by columns rather than rows, which enables better compression and more efficient queries for analytical workloads. It supports nested data structures and is optimized for handling complex data.

Advantages

• Highly efficient columnar storage and compression
• Excellent query performance for analytical workloads
• Support for nested data structures
• Schema evolution capabilities

Limitations

• Not human-readable like CSV or JSON
• Less suitable for row-oriented operations
• Requires specialized tools for viewing and editing
• More complex than simpler formats

Apache Arrow is a cross-language platform for in-memory data. It defines a standard columnar memory format for flat and hierarchical data that works across different programming languages. This format is optimized for efficient analytics on modern hardware like CPUs and GPUs.

Arrow can handle complex nested data structures and lets you query and work with specific columns without reading the entire dataset.

Key Features

• Columnar memory format for flat and hierarchical data
• Works with any programming language
• Optimized for analytics and modern hardware
• Supports complex nested data structures
• Enables efficient zero-copy reads

Use Cases

Apache Arrow shines in scenarios like:

• Big data processing and analytics
• Machine learning and AI pipelines
• Data exchange between different systems and languages
• High-performance computing applications

Its efficient memory layout and standardized format make it a great choice for applications that need fast data processing and compatibility between different tools and languages.