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DataFreeOpen Source
APACHE ARROW
Universal columnar format for fast data interchange
Apache-2.0
ABOUT
Data scientists and ML engineers waste significant time and memory on serialization overhead when moving data between different languages, tools, and frameworks. Traditional row-based formats like CSV and JSON are slow to parse, and each library uses its own in-memory representation, forcing costly copies and conversions for even simple data pipelines.
INSTALL
pip install pyarrowINTEGRATION GUIDE
1. Read and write large Parquet, CSV, and JSON datasets with zero-copy columnar access for ML training pipelines
2. Share data between Python, R, C++, and Java applications without serialization overhead using the Arrow IPC format
3. Perform high-performance in-memory analytics on large datasets using Arrow compute kernels and dataset APIs
TAGS
columnar-formatdata-interchangein-memory-analyticsparquetdata-engineering