When data exceeds a single machine's memory, you need strategies that decompose computation into independent chunks. Batch processing, reservoir sampling, and online statistics are the building blocks of scalable pipelines.
Learning Objectives
→Process a dataset in memory-bounded mini-batches
→Implement reservoir sampling for uniform random sampling over a stream
→Compute running mean and variance incrementally with Welford's algorithm
→Merge partial statistics from independent data shards