PySpark

About PySpark

Python API for Apache Spark, enabling scalable and efficient data processing. Particularly useful for ETL processes involving large datasets that need parallel processing across a cluster.

Key Features

• 1Distributed DataFrame API that mirrors pandas for big data at scale
• 2Spark SQL engine for running SQL queries on distributed datasets
• 3Structured Streaming for real-time data processing pipelines
• 4MLlib integration for distributed machine learning workflows
• 5Native connectors for S3, HDFS, Delta Lake, Kafka, and JDBC

How Python Data Engineers Use PySpark

PySpark is the standard Python interface for large-scale ETL on Hadoop and cloud clusters. Data engineers write transformation logic using the DataFrame API — reading from S3 or Hive, applying joins and aggregations, then writing to Delta Lake or a data warehouse — with Spark distributing the work across hundreds of nodes.