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 3.0, this second 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, SQL, Scala, or Java high-level Structured APIs
Understand Spark operations and SQL Engine
Inspect, tune, and debug Spark operations with Spark configurations and Spark UI
Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka
Perform analytics on batch and streaming data using Structured Streaming
Build reliable data pipelines with open source Delta Lake and Spark
Develop machine learning pipelines with MLlib and productionize models using MLflow