Scala and Spark for Big Data Analytics: Explore the concepts of functional programming, data streaming, and machine learning - Softcover

Karim, Md. Rezaul; Alla, Sridhar

Softcover

ISBN 10: 1785280848 ISBN 13: 9781785280849

Verlag: Packt Publishing, 2017

Über diesen Titel

Reseña del editor

Harness the power of Scala to program Spark and analyze tons of data in the blink of an eye

Key Features

Experience Scala's sophisticated type system, combining functional programming and object-oriented concepts

Work on an array of applications, ranging from simple batch jobs to stream processing and machine learning

Perform large-scale data analysis by exploring both common as well as complex use-cases

Book Description

Scala has been witnessing wide-scale adoption over the past few years, particularly in the field of data science and analytics. Spark, which is built on Scala, has also gained recognition, and is now being used widely in production. This book is designed to help you leverage the power of Scala and Spark to make sense of big data.

Scala and Spark for Big Data Analytics begins by introducing you to Scala and helping you understand the object-oriented and functional programming concepts required for Spark application development. You'll then move onto Spark and cover basic abstractions using Resilient Distributed Dataset (RDD) and DataFrame. This will help you develop scalable, fault-tolerant streaming applications by analyzing structured and unstructured data using SparkSQL, GraphX, and Spark structured streaming. In the sections to follow, you'll explore advanced topics, such as monitoring, configuration, debugging, testing, and deployment, which will further help you to manage your data effectively.

After this, you'll learn to use SparkR and PySpark APIs to develop impactful applications, and deploy Zeppelin to help you create interactive data analytics. Towards the concluding chapters, you'll be able to use Alluxio to facilitate in-memory data processing.

By the end of this book, you'll have a clear understanding of Spark and be able to perform full-stack data analytics regardless of the amount of data.

What you will learn

• Get an in-depth understanding of Scala collection APIs
• Work with RDD and DataFrame to learn Spark's core abstractions
• Analyse structured and unstructured data using SparkSQL and GraphX
• Build scalable and fault-tolerant streaming applications using Spark structured streaming
• Discover machine-learning (ML) best practices for classification, regression, dimensionality reduction, and recommendation system to build predictive models with widely used algorithms in Spark MLlib and ML
• Develop clustering models to cluster a vast amount of data
• Get to grips with tuning, debugging, and monitoring Spark applications
• Deploy Spark applications on real clusters in Standalone, Mesos, and Yet Another Resource Negotiator (YARN)

Who this book is for

If you want to learn how to perform data analysis by harnessing the power of Spark, this is the book for you. Prior knowledge of Spark or Scala is not required. Programming experience (particularly with other Java virtual machine(JVM) languages) will be useful to help you grasp the concepts easily.

Table of Contents

1. Introduction to Scala
2. Object-Oriented Scala
3. Functional Programming Concepts
4. Collection APIs
5. Tackle Big Data - Spark Comes to the Party
6. Start Working with Spark - REPL and RDDs
7. Special RDD Operations
8. Introduce a Little Structure - Spark SQL
9. Stream Me Up, Scotty - Spark Streaming
10. Everything is Connected - GraphX
11. Learning Machine Learning - Spark MLlib and Spark ML
12. My Name is Bayes, Naive Bayes
13. Time to Put Some Order - Cluster Your Data with Spark MLlib
14. Text Analytics Using Spark ML
15. Spark Tuning
16. Time to Go to ClusterLand - Deploying Spark on a Cluster
17. Testing and Debugging Spark
18. PySpark and SparkR

Biografía del autor

Md. Rezaul Karim is a research scientist at Fraunhofer FIT, Germany. He is also a PhD candidate at RWTH Aachen University, Aachen, Germany. He holds a BSc and an MSc in computer science. Before joining Fraunhofer FIT, he had been working as a researcher at the Insight Centre for data analytics, Ireland. Previously, he worked as a lead engineer with Samsung Electronics' distributed R&D centers in Korea, India, Vietnam, Turkey, and Bangladesh. Earlier, he worked as a research assistant in the Database Lab at Kyung Hee University, Korea, and as an R&D engineer with BMTech21 Worldwide, Korea. Even before that, he worked as a software engineer with i2SoftTechnology, Dhaka, Bangladesh. He has more than 8 years of experience in the area of research and development, with a solid knowledge of algorithms and data structures in C/C++, Java, Scala, R, and Python-focused big data technologies: Spark, Kafka, DC/OS, Docker, Mesos, Zeppelin, Hadoop, and MapReduce, and deep learning technologies: TensorFlow, DeepLearning4j, and H2O-Sparking Water. His research interests include machine learning, deep learning, semantic web, linked data, big data, and bioinformatics. He is the author of the following book titles with Packt: Large-Scale Machine Learning with Spark Deep Learning with TensorFlow Sridhar Alla is a big data expert helping small and big companies solve complex problems, such as data warehousing, governance, security, real-time processing, high-frequency trading, and establishing large-scale data science practices. He is an agile practitioner as well as a certified agile DevOps practitioner and implementer. He started his career as a storage software engineer at Network Appliance, Sunnyvale, and then worked as the chief technology officer at a cyber security firm, eIQNetworks, Boston. His job profile includes the role of the director of data science and engineering at Comcast, Philadelphia. He is an avid presenter at numerous Strata, Hadoop World, Spark Summit, and other conferences. He also provides onsite/online training on several technologies. He has several patents filed in the US PTO on large-scale computing and distributed systems. He holds a bachelors degree in computer science from JNTU, Hyderabad, India, and lives with his wife in New Jersey. Sridhar has over 18 years of experience writing code in Scala, Java, C, C++, Python, R and Go. He also has extensive hands-on knowledge of Spark, Hadoop, Cassandra, HBase, MongoDB, Riak, Redis, Zeppelin, Mesos, Docker, Kafka, ElasticSearch, Solr, H2O, machine learning, text analytics, distributed computing and high performance computing.

Details zur Ausgabe

• Verlag: Packt Publishing
• Erscheinungsdatum: 2017
• ISBN 10: 1785280848
• ISBN 13: 9781785280849
• Einband: Tapa blanda
• Sprache: Englisch
• Anzahl der Seiten: 786