Data Engineering with Apache Spark / PySpark

Apache Spark and PySpark handle the heavy lifting when datasets exceed what single-node processing can manage. I use Spark for distributed batch processing, streaming analytics, and large-scale data transformations — from investment portfolio analysis with sliding-window computations to marketing analytics processing hundreds of millions of daily events. For teams hitting performance ceilings with pandas or traditional SQL, Spark provides the distributed computing foundation to scale.