Convert Arrow to Parquet

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.

Apache Parquet is a columnar storage format that works great with big data processing frameworks. It compresses and encodes data efficiently, which saves a lot of storage space and makes reading data faster.

Parquet can handle complex nested data structures. It lets you query and work with specific columns without having to read the entire dataset.

Compression

Parquet supports several compression algorithms like Snappy, Gzip, and LZO. These compression techniques help reduce storage space and speed up data processing tasks.