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Apache Spark 2: Data Processing and Real-Time Analytics
Apache Spark 2: Data Processing and Real-Time Analytics

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

By Romeo Kienzler , Md. Rezaul Karim , Sridhar Alla , Siamak Amirghodsi , Meenakshi Rajendran , Broderick Hall , Shuen Mei
$15.99 per month
Book Dec 2018 pages 1st Edition

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Key benefits

  • Master the art of real-time big data processing and machine learning
  • Explore a wide range of use-cases to analyze large data
  • Discover ways to optimize your work by using many features of Spark 2.x and Scala

Description

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

Product Details

Country selected

Publication date : Dec 21, 2018
Length pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789959208
Category :

What do you get with a Packt Subscription?

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Product Details


Publication date : Dec 21, 2018
Length pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781789959208
Category :

Table of Contents

23 Chapters
Title Page Chevron down icon Chevron up icon
Copyright Chevron down icon Chevron up icon
About Packt Chevron down icon Chevron up icon
Contributors Chevron down icon Chevron up icon
Preface Chevron down icon Chevron up icon
1. A First Taste and What's New in Apache Spark V2 Chevron down icon Chevron up icon
2. Apache Spark Streaming Chevron down icon Chevron up icon
3. Structured Streaming Chevron down icon Chevron up icon
4. Apache Spark MLlib Chevron down icon Chevron up icon
5. Apache SparkML Chevron down icon Chevron up icon
6. Apache SystemML Chevron down icon Chevron up icon
7. Apache Spark GraphX Chevron down icon Chevron up icon
8. Spark Tuning Chevron down icon Chevron up icon
9. Testing and Debugging Spark Chevron down icon Chevron up icon
10. Practical Machine Learning with Spark Using Scala Chevron down icon Chevron up icon
11. Spark's Three Data Musketeers for Machine Learning - Perfect Together Chevron down icon Chevron up icon
12. Common Recipes for Implementing a Robust Machine Learning System Chevron down icon Chevron up icon
13. Recommendation Engine that Scales with Spark Chevron down icon Chevron up icon
14. Unsupervised Clustering with Apache Spark 2.0 Chevron down icon Chevron up icon
15. Implementing Text Analytics with Spark 2.0 ML Library Chevron down icon Chevron up icon
16. Spark Streaming and Machine Learning Library Chevron down icon Chevron up icon
1. Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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