Lessons Learned from Scaling to Multi-Terabyte Datasets

This post provides insights into scaling to multi-terabyte datasets, focusing on lessons learned and tools for efficient scaling. It emphasizes the importance of evaluating algorithms before scaling and offers guidance on scaling on single machines using tools like Joblib and GNU Parallel. Joblib is highlighted for parallelizing workloads, while GNU Parallel is recommended for CLI data processing tasks. The post also delves into scaling to multiple machines, discussing when to transition to multiple machines and the benefits of horizontal scaling. It compares the performance and cost implications of using multiple smaller instances versus a single large instance. Additionally, it touches on different computing models for embarrassingly parallel workloads and analytical workloads, recommending tools like AWS Batch, Dask, and Spark for scaling up computations. The post concludes by suggesting considerations for selecting the appropriate tool based on team size, infrastructure, and workload requirements.