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This repo contains implementations of PySpark for real-world use cases for batch data processing, streaming data processing sourced from Kafka, sockets, etc., spark optimizations, business specific bigdata processing scenario solutions, and machine learning use cases.
Production-grade real-time ELT pipeline using PySpark Structured Streaming and Delta Lake. Replicates a high-impact architectural migration from Mercedes-Benz to achieve exactly-once upsert semantics and 60% reduction in cloud compute overhead.
Repository for practicing data manipulation and transformation using PySpark. Contains sample scripts for data pipelining, showcasing various techniques and best practices for handling and processing large datasets efficiently.