Sept. 2, 2021
Data
in all domains is getting bigger. How can you work with it efficiently? This
book introduces Apache Spark, the open source cluster computing system that
makes data analytics fast to write and fast to run. With Spark, you can tackle
big datasets quickly through simple APIs in Python, Java, and Scala.
Written
by the developers of Spark, this book will have data scientists and engineers
up and running in no time. You’ll learn how to express parallel jobs with just
a few lines of code, and cover applications from simple batch jobs to stream
processing and machine learning.
■■ Quickly dive into Spark capabilities such as distributed datasets,
in-memory caching, and the interactive shell
■■ Leverage Spark’s powerful built-in libraries, including Spark SQL,
Spark Streaming, and MLlib
■■ Use one programming paradigm instead of mixing and matching tools
like Hive, Hadoop, Mahout, and Storm
■■ Learn how to deploy interactive, batch, and streaming applications
■■ Connect to data sources including HDFS, Hive, JSON, and S3
■■ Master advanced topics like data partitioning and shared variables
Holden
Karau, a software development engineer at
Databricks, is active in open source and the author of Fast Data Processing with Spark (Packt Publishing).
Andy
Konwinski, co-founder of Databricks, is a
committer on Apache Spark and co-creator of the Apache Mesos project.
Patrick
Wendell is a co-founder of Databricks and a
committer on Apache Spark. He also maintains several subsystems of Spark’s core
engine.
Matei
Zaharia, CTO at Databricks, is the creator of
Apache Spark and serves as its Vice President at Apache.
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