Apache Spark 2: Data Processing and Real-Time Analytics: Master complex big data processing,stream analytics,and machine learning with Apache Spark
Authors: Romeo Kienzler – Md. Rezaul Karim – Sridhar Alla – Siamak Amirghodsi – Meenakshi Rajendran – Broderick Hall – Shuen Mei
ISBN-10: 1789959209
ISBN-13: 9781789959208
Released: 2018-12-21
Print Length 页数: 616 pages
Book Description
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
contents
1 A First Taste and What’s New in Apache Spark V2
2 Apache Spark Streaming
3 Structured Streaming
4 Apache Spark MLlib
5 Apache SparkML
6 Apache SystemML
7 Apache Spark GraphX
8 Spark Tuning
9 Testing and Debugging Spark
10 Practical Machine Learning with Spark Using Scala
11 Spark’s Three Data Musketeers for Machine Learning – Perfect Together
12 Common Recipes for Implementing a Robust Machine Learning System
13 Recommendation Engine that Scales with Spark
14 Unsupervised Clustering with Apache Spark 2.0
15 Implementing Text Analytics with Spark 2.0 ML Library
16 Spark Streaming and Machine Learning Library
