When working with big data, PySpark is commonly used for data preprocessing and transformation at scale. While built-in PySpark ML transformers like StandardScaler or StringIndexer handle many common tasks, real-world use cases often require custom transformations tailored to your data and domain. That’s where custom PySpark ML transformers come in, allowing you to embed custom […]
