Compare Apache DataFusion vs. StoneFusion in 2025

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Description

Apache DataFusion is a versatile and efficient query engine crafted in Rust, leveraging Apache Arrow for its in-memory data representation. It caters to developers engaged in creating data-focused systems, including databases, data frames, machine learning models, and real-time streaming applications. With its SQL and DataFrame APIs, DataFusion features a vectorized, multi-threaded execution engine that processes data streams efficiently and supports various partitioned data sources. It is compatible with several native formats such as CSV, Parquet, JSON, and Avro, and facilitates smooth integration with popular object storage solutions like AWS S3, Azure Blob Storage, and Google Cloud Storage. The architecture includes a robust query planner and an advanced optimizer that boasts capabilities such as expression coercion, simplification, and optimizations that consider distribution and sorting, along with automatic reordering of joins. Furthermore, DataFusion allows for extensive customization, enabling developers to incorporate user-defined scalar, aggregate, and window functions along with custom data sources and query languages, making it a powerful tool for diverse data processing needs. This adaptability ensures that developers can tailor the engine to fit their unique use cases effectively.

Description

StoneFly's StoneFusion™ converts bare-metal systems into a comprehensive enterprise solution that includes iSCSI SAN, NAS, S3 object storage, or a unified storage appliance, complete with built-in ransomware defense, storage optimization features, and data monitoring services. Additionally, StoneFusion can be utilized within Azure, AWS, and the StoneFly cloud environments, providing flexibility for various deployment needs.