# Apache DataFusion vs Delta-rs (Delta Lake Rust): 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: Apache DataFusion wins as the high-performance vectorized SQL and DataFrame query planner/executor built on Rust Apache Arrow across 9.5k+ GitHub stars (97.9), while Delta-rs (`deltalake` Python package) wins as the foundational Rust ACID storage and transaction-log layer that brings zero-JVM Delta Lake reads, writes, merges, and Z-Ordering directly into DataFusion, Polars, and DuckDB across 3.3k+ GitHub stars (97.3).

- **Category**: Vectorized Rust Apache Arrow Query Engine vs Native Rust ACID Delta Lake Storage Layer Shootout
- **Canonical URL**: https://exactbaseline.com/vs/datafusion-vs-delta-rs
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## Apache DataFusion Key Advantages
- 9,488+ GitHub stars under Apache-2.0 powering InfluxDB 3.0, ParadeDB, LanceDB, GreptimeDB, and VegaFusion with sub-millisecond Rust query planning
- Full ANSI SQL parser, cost-based optimizer, spill-to-disk hash/sort operators, and Python `datafusion` DataFrame bindings
- Runs 6.8x faster than single-node JVM Spark with zero garbage-collection tail-latency spikes

## Delta-rs (Delta Lake Rust) Key Advantages
- 3,343+ GitHub stars under Apache-2.0 providing `pip install deltalake` to read, write, `MERGE`, `OPTIMIZE`, and `VACUUM` Delta tables without Java/Spark
- Registers directly as a native `TableProvider` inside Apache DataFusion so SQL queries push down partition and parquet row-group predicates automatically
- Enables serverless AWS Lambda and lightweight Kubernetes pods (128MB RAM) to commit ACID Lakehouse transactions