direct evidenceDistributed processing benchmark
Distributed Data Processing & Scalability Analysis
Distributed PySpark ETL and aggregation on NYC Taxi data, benchmarked in reproducible single-node and multi-node Docker environments.
Documented result2.3× throughput gain in the compared multi-node setupSingle-node versus multi-node Spark benchmark (local research; completed): Reproducible Docker environments running PySpark cleaning, transformation and aggregation workflows; compared throughput, runtime and CPU/memory utilization alongside distributed-execution overhead. NYC Taxi dataset; dataset volume is not supplied. Multi-node relative throughput gain: 2.3×. Reported multi-node throughput relative to the compared single-node Spark setup on NYC Taxi data in a local Docker experiment. Absolute throughput, runtime, hardware, node count and dataset volume are not supplied; no production performance or cost savings are implied. Local distributed experiment, not a production data platform or cloud-managed Spark deployment. The reported 2.3× throughput gain applies only to the compared multi-node and single-node setups; it is not a general Spark performance guarantee. Dataset volume, cluster node count, worker configuration, hardware, absolute runtime values, cost figures and detailed benchmark tables are not supplied.