Book Image

Big Data Analytics

By : Venkat Ankam
Book Image

Big Data Analytics

By: Venkat Ankam

Overview of this book

Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.
Table of Contents (18 chapters)
Big Data Analytics
About the Author
About the Reviewers

Machine learning with H2O and Spark

H2O is an open source system for machine learning. It offers a rich set of machine learning algorithms and a web-based data processing user interface. It offers the ability to develop machine learning applications in Java, Scala, Python, and R. It also has the ability to interface with Spark, HDFS, Amazon S3, SQL, and NoSQL databases. H2O also provides an H2O Flow, which is an IPython-like notebook that allows you to combine code execution, text, mathematics, plots, and rich media into a single document. Sparkling Water is a product of H2O on Spark.

Why Sparkling Water?

Sparkling Water combines the best of both worlds of Spark and H2O:

  • Spark provides the best APIs, RDDs, and multitenant contexts

  • H2O provides speed, columnar-compression, machine learning, and deep learning algorithms

  • Both Spark and H2O Contexts reside in a shared executor JVM and shared Spark RDDs and H2O RDDs

An application flow on YARN

The steps involved in a Sparkling Water application submitted...