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Scala and Spark for Big Data Analytics

Last updated: 08-10-2018

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Scala and Spark for Big Data Analytics

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Scala and Spark for Big Data Analytics
Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye!
Scala and Spark for Big Data Analytics
Md. Rezaul Karim, Sridhar Alla
July 2017
4 customer reviews
Harness the power of Scala to program Spark and analyze tonnes of data in the blink of an eye!
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Scala and Spark for Big Data Analytics
$ 51.99
Mastering Apache Spark 2.x - Second Edition
$ 39.99
Paperback898 pages
Book Description
Scala has been observing wide adoption over the past few years, especially in the field of data science and analytics. Spark, built on Scala, has gained a lot of recognition and is being used widely in productions. Thus, if you want to leverage the power of Scala and Spark to make sense of big data, this book is for you.
The first part introduces you to Scala, helping you understand the object-oriented and functional programming concepts needed for Spark application development. It then moves on to Spark to cover the basic abstractions using RDD and DataFrame. This will help you develop scalable and fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. Finally, the book moves on to some advanced topics, such as monitoring, configuration, debugging, testing, and deployment.
You will also learn how to develop Spark applications using SparkR and PySpark APIs, interactive data analytics using Zeppelin, and in-memory data processing with Alluxio.
By the end of this book, you will have a thorough understanding of Spark, and you will be able to perform full-stack data analytics with a feel that no amount of data is too big.
Table of Contents
Understand object-oriented & functional programming concepts of Scala
In-depth understanding of Scala collection APIs
Work with RDD and DataFrame to learn Spark’s core abstractions
Analysing structured and unstructured data using SparkSQL and GraphX
Scalable and fault-tolerant streaming application development using Spark structured streaming
Learn machine-learning best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib & ML
Build clustering models to cluster a vast amount of data
Understand tuning, debugging, and monitoring Spark applications
Deploy Spark applications on real clusters in Standalone, Mesos, and YARN
Authors
Md. Rezaul Karim
Md. Rezaul Karim is a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Germany. Before joining FIT, he worked as a researcher at the Insight Center for Data Analytics, Ireland. He was a lead engineer at Samsung Electronics, Korea. He has 9 years' R&D experience with C++, Java, R, Scala, and Python. He has published research papers on bioinformatics, big data, and deep learning. He has practical working experience with Spark, Zeppelin, Hadoop, Keras, Scikit-Learn, TensorFlow, Deeplearning4j, MXNet, and H2O.
Sridhar Alla
Sridhar Alla is a big data expert helping companies solve complex problems in distributed computing, large scale data science and analytics practice. He presents regularly at several prestigious conferences and provides training and consulting to companies. He holds a bachelor's in computer science from JNTU, India.
He loves writing code in Python, Scala, and Java. He also has extensive hands-on knowledge of several Hadoop-based technologies, TensorFlow, NoSQL, IoT, and deep learning.
Table of Contents


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