Learning Spark

Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark.Updated to include Spark 4.0, this third edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you'll be able to:Learn Python and SQL high-level Structured APIsUnderstand Spark operations and SQL EngineInspect, tune, and debug Spark operations with Spark configurations and Spark UIConnect to data sources: JSON, CSV, Protobuf, Parquet, S3, Kafka, Delta Lake, and Apache IcebergLearn to work with modern lakehouse catalogsPerform analytics on batch and streaming data using Structured StreamingBuild reliable data pipelines with open source Delta Lake, Apache Iceberg, and SparkDevelop machine learning pipelines with MLlib and productionize models using MLflow