#data pipelines

Many anti-money laundering (AML) engines underperform or generate excessive false positives because of the scale and complexity of modern financial data. These unsatisfactory results are typically not due to flawed detection logic but rather to insufficient supporting infrastructure. A variety of infrastructure limitations, such as weak data pipelines, limited compute... Read more » The post How the Right Infrastructure Unlocks Better AML Engine Performance appeared first on Big Data Analytics News.

Modern data pipelines handle massive volumes of structured and unstructured data every day. As datasets grow, poorly optimized Spark jobs become slower, more expensive, and harder to scale. Common issues include long execution times, excessive shuffling, memory bottlenecks, and inefficient joins. Effective PySpark optimization can significantly improve performance, reduce infrastructure costs, and enhance cluster efficiency. […] The post PySpark Optimization: 12 Proven Techniques to Speed Up Your Spark Jobs appeared first on Analytics Vidhya.