Efficient Engines for Different Workloads

Not all workloads benefit from GPUs, with some workloads like DuckDB and DataFusion being preferred in many cases. As systems evolve to be faster, these efficient engines will remain relevant. At a multi-terabyte scale, heavily intensive ETL workloads see significant benefits from hardware-accelerated computing, while smaller workloads do not require this level of performance. The multi-engine data stack concept suggests using efficient engines for small to medium data, then transitioning to hardware acceleration for large datasets, leading to substantial speed improvements, cost savings, and reduced carbon emissions in computing.