2025: The Year of 1,000 DataFusion-Based Systems (www.influxdata.com)

Apache DataFusion has hit a maturity inflection: elevated to an Apache top-level project, published at ACM SIGMOD, attracted major corporate contributors (Apple, eBay, TikTok, Alibaba) and community growth, and with DataFusion 43.0.0 becoming the fastest engine for querying Apache Parquet in ClickBench. The author predicts 2025 will be the year DataFusion crosses 1,000 projects as Rust-based, Arrow-native, vectorized execution becomes a go-to building block for high-performance analytics—already running tens of millions of plans per day in InfluxDB 3 and underpinning Rust implementations of Delta Lake, Iceberg and Hudi. For the AI/ML community this matters because DataFusion optimizes the core I/O and query layers that feed model training, feature stores and iterative analytics: fast Parquet/Arrow access, vectorized operators, and a permissive, extensible stack lower engineering costs and enable real-time/large-scale pipelines on open data lakes. Key technical directions in 2025 include simplifying remote file queries, advanced caching, continued vectorized performance work (group keys, pruning), stronger automated testing (SQLite test corpus, SQLancer) and smoother upstream compatibility for downstream projects. The project’s momentum—industry contributors, Apple’s Comet work, and focused bug-harding—signals a practical, high-performance alternative to legacy C/C++ engines that AI teams should evaluate for scalable data preprocessing and analytics.