Scala and Spark for Big Data Analytics

More Information
Learn
  • 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
About

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.

Features
  • Learn Scala’s sophisticated type system that combines Functional Programming and object-oriented concepts
  • Work on a wide array of applications, from simple batch jobs to stream processing and machine learning
  • Explore the most common as well as some complex use-cases to perform large-scale data analysis with Spark
Page Count 898
Course Length 26 hours 56 minutes
ISBN 9781785280849
Date Of Publication 24 Jul 2017

Authors

Md. Rezaul Karim

Md. Rezaul Karim 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 focusing 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 a Research Scientist at Fraunhofer Institute for Applied Information Technology-FIT, Germany. He is also a Ph.D. candidate at the RWTH Aachen University, Aachen, Germany.

He holds a BS and an MS degree in Computer Engineering. Before joining the Fraunhofer-FIT, he had been working as a Researcher at the Insight Centre for Data Analytics, Ireland. Before that, he worked as a Lead Engineer with Samsung Electronics’ distributed R&D Institutes in Korea, India, Vietnam, Turkey, and Bangladesh.

Before that, he worked as a Research Assistant in the Database Lab at Kyung Hee University, Korea. He also worked as an R&D Engineer with BMTech21 Worldwide, Korea. Even before that, he worked as a Software Engineer with i2SoftTechnology, Dhaka, Bangladesh.

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 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.