Accelerating Iceberg Analytics: How Apache Arrow Can Help get the best out of SIMD for Breakneck Speed
Modern analytics engines need to squeeze every drop of performance out of the CPU. We often hear that SIMD (Single Instruction Multiple Data) can accelerate computation by processing multiple data points in a single CPU instruction. And that’s true—provided the processor isn’t left twiddling its thumbs waiting for data from memory. To unlock the full benefit of SIMD (or any high-performance CPU operation), we must design our data pipelines to efficiently feed data to the CPU.
This post explores how this principle can be applied when building analytics on top of object storage native open-table formats like Apache Iceberg tables, which store data in Parquet files on disk, and how in-memory formats like Apache Arrow drastically reduce wasted CPU cycles. It also discussed why Rust is becoming a first-class citizen in the data engineering world, tying these components together in next-generation analytics engines.
Most Iceberg tables store data in Apache Parquet format. Parquet is a columnar, compressed file format optimized for efficiently reading large swaths of data on disk. It helps techniques like column pruning (scan only needed columns) and compresses column data very effectively, which means less I/O when reading from disk. These features make Parquet an excellent “at rest” format.
