Andy Grove discusses his mission to build the Big Data ecosystem in Rust and the challenges he faced. He talks about using Rust for big data and building a Rust implementation of Apache Arrow. The podcast also explores optimizing Spark's performance and the future of composable data systems. It highlights exciting developments in Data Fusion and the need for better benchmark results.
Rust offers high performance and memory safety advantages for building data processing engines.
Apache Arrow enables building composable and interoperable data systems across different languages.
Deep dives
Introducing Andy Grove and his Background in Software Development
Andy Grove, creator of the Data Fusion Rust Library, shares his background as a software developer and his journey in building query engines.
The Appeal of Rust for Building Apache Spark-like Systems
Andy explains why he chose Rust for building a data processing engine like Apache Spark. He highlights Rust's performance similar to C++ and memory safety advantages compared to Java.
The Significance of Apache Arrow for Columnar Data Processing
Andy discusses the importance of Apache Arrow, a memory specification for columnar data, and its role in building composable and interoperable data systems across different languages.
The Future of Composable Data Systems and the Role of Rust
Andy envisions a future where more companies build their own data systems using composable components like Data Fusion, enabling seamless integration and faster innovation in the data space. He also discusses the potential of WebAssembly (Wasm) for enhancing interoperability and the role of GPUs in accelerating data infrastructure, particularly in Spark.
Ian and Tim sat down with Andy Grove (creator Datafusion, Apache Arrow PMC chair) to talk about his original mission to build the Big Data ecosystem in Rust, and how that has evolved until now.
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