Introduction to Apache Spark: a unified analytics engine -- Downloading Apache Spark and getting started -- Apache Spark's structured APIs -- Spark SQL and DataFrames: introduction to built-in data sources -- Spark SQL and DataFrames: interacting with external data sources -- Spark SQL and Datasets -- Optimizing and tuning Spark applications -- Structured streaming -- Building reliable data lakes with Apache Spark -- Machine learning with MLib -- Managing, deploying, and scaling machine learning pipelines with Apache Spark -- Epilogue: Apache Spark 3.0.
Summary:
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.
This resource is supported by the Institute of Museum and Library Services under the provisions of the Library Services and Technology Act as administered by State Library of Iowa.