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Show me evidence of Distributed Data Processing

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Distributed Data Processing & Scalability Analysis: Distributed PySpark ETL and aggregation on NYC Taxi data, benchmarked in reproducible single-node and multi-node Docker environments. 2.3× Multi-node relative throughput gain · local research. NYC Taxi dataset; dataset volume is not supplied. Python, PySpark, Apache Spark and Docker; distributed cleaning, transformation and aggregation with runtime, throughput and CPU/memory utilization comparisons. 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.

[1] Distributed processing benchmark

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.

Here’s the evidence.

Distributed Data Processing & Scalability Analysis

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