Kanzi
A modern, modular, and efficient lossless data compressor in C++ that combines multiple algorithms and multi-threading for high performance.
What is Kanzi?
Kanzi is a modern lossless data compressor written in C++ that combines multiple compression algorithms like Burrows-Wheeler Transform (BWT) and Context Modeling (CM) to achieve higher compression ratios than traditional LZ-based tools. It solves the problem of underutilized modern hardware by implementing built-in multi-threading to compress data in parallel across CPU cores, making it efficient for high-performance scenarios like backups and real-time data transfers.
Developers and engineers working with large datasets who need high-performance compression beyond standard tools, particularly those dealing with diverse data types (multimedia, DNA, text) or requiring customizable compression pipelines.
Developers choose Kanzi for its ability to achieve compression ratios unattainable with traditional LZ methods while maintaining performance through multi-core utilization. Its modular, extensible architecture with no external dependencies makes it uniquely adaptable for specialized data types and experimental compression techniques.
Overview
Fast lossless data compression in C++
Use Cases
Best For
- Achieving higher compression ratios than zstd or brotli for specific data types
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