PySpark for Beginners: Mastering the Basics

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.

pandas remains the default choice for notebooks, exploratory analysis, visualization, and machine learning workflows. Polars focus on fast, memory-efficient DataFrame processing, while DuckDB brings a SQL-first approach for querying local files and embedded analytics. Each tool fits a different kind of local data workflow. In this article, we compare pandas, Polars, and DuckDB across performance, […] The post Pandas vs Polars vs DuckDB: Which Library Should You Choose? appeared first on Analytics Vidhya.

How we replaced Python pipelines with dlt, dbt, and Trino — and cut delivery time from weeks to one day. The post 4 YAML Files Instead of PySpark: How We Let Analysts Build Data Pipelines Without Engineers appeared first on Towards Data Science.