Apache Spark 2: Data Processing and Real-Time Analytics: Master complex big data processing, stream analytics, and machine learning with Apache Spark

Apache Spark 2: Data Processing and Real-Time Analytics: Master complex big data processing, stream analytics, and machine learning with Apache SparkReviews
Author: Romeo Kienzler
Pub Date: 2018
ISBN: 978-1789959208
Pages: 616
Language: English
Format: EPUB
Size: 532 Mb

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Build efficient data flow and machine learning programs with this flexible, multi-functional open-source cluster-computing framework
Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark’s functionality and building your own data flow and machine learning programs on this platform.
You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools.
By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle.
This Learning Path includes content from the following Packt products:

  • Mastering Apache Spark 2.x by Romeo Kienzler
  • Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla
  • Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook

What you will learn

  • Get to grips with all the features of Apache Spark 2.x
  • Perform highly optimized real-time big data processing
  • Use ML and DL techniques with Spark MLlib and third-party tools
  • Analyze structured and unstructured data using SparkSQL and GraphX
  • Understand tuning, debugging, and monitoring of big data applications
  • Build scalable and fault-tolerant streaming applications
  • Develop scalable recommendation engines