• Title: Learning Apache Spark with Python
• Author(s) Wenqiang Feng
• Publisher: Self-Publishing (GitHub, 2021); eBook (Creative Commons Licensed)
• License(s): Creative Commons License (CC)
• Paperback: N/A
• eBook: PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This book offers an introduction to the Apache Spark ecosystem, you will learn a wide array of concepts about PySpark in Data Mining, Text Mining, Machine Learning and Deep Learning.

• N/A

• Amazon

• Big Data
• Python Programming
• Data Analysis and Data Mining
• Data Engineering
• Data Science

• Learning Apache Spark with Python (Wenqiang Feng)
• The Mirror Site (1) - PDF

• Learning Spark: Lightning-Fast Data Analytics (Jules Damji, et al.)

  This book shows data engineers and data scientists why structure and unification in Apache Spark matters. Specifically, it explains how to perform simple and complex data analytics and employ machine learning algorithms.

• Mastering Spark with R: Large-Scale Analysis and Modeling

  With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Apache Spark from R to tackle big data and big compute problems.

• Using Spark from R for Performance with Arbitrary Code

  This short publication attempts to provide practical insights into using the sparklyr interface to gain the benefits of Apache Spark while still retaining the ability to use R code organized in custom-built functions and packages.

• The Internals of Apache Spark (Jacek Laskowski)

  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.

• The Data Engineer’s Guide to Apache Spark (Databricks)

  This book is for data engineers looking to leverage the immense growth of Apache Spark to build faster and more reliable data pipelines. It leverages Spark's amazing speed, scalability, simplicity, and versatility to build practical Big Data solutions.

• Big Data Processing with Apache Spark (Srini Penchikala)

  Learn about the Apache Spark framework and develop Spark programs for use cases in big-data analysis. It covers all the libraries that are part of Spark ecosystem, which includes Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and Spark GraphX.

• Graph Algorithms: Practical Examples in Apache Spark and Neo4j

  This is a practical guide to getting started with graph algorithms for developers and data scientists who have experience using Apache Spark or Neo4j. You'll walk through hands-on examples that show you how to use graph algorithms in Apache Spark/Neo4j.