Convert Parquet to Arrow

Apache Parquet is a columnar storage format optimized for use with big data processing frameworks. It offers efficient data compression and encoding schemes, which leads to significant storage savings and improved read performance.

Parquet is designed to support complex nested data structures and enables efficient querying and manipulation of specific columns without reading the entire dataset.

Compression

Parquet supports various compression algorithms such as Snappy, Gzip, and LZO. These compression techniques help in reducing the storage space and improving the performance of data processing tasks.

Apache Arrow is a cross-language development platform for in-memory data that specifies a standardized language-independent columnar memory format for flat and hierarchical data, organized for efficient analytic operations on modern hardware like CPUs and GPUs.

Arrow is designed to support complex nested data structures and enables efficient querying and manipulation of specific columns without reading the entire dataset.

Key Features

• Columnar memory format for flat and hierarchical data
• Language-agnostic specification
• Optimized for analytical processing and modern hardware
• Support for complex nested data structures
• Efficient zero-copy reads

Use Cases

Apache Arrow is particularly useful in scenarios involving:

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

Its efficient memory layout and standardized format make it an excellent choice for applications requiring fast data processing and interoperability between different tools and languages.