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 is the science of making machines work without programming predefined rules and learn from data. It is used to build regression, classification, clustering, anomaly detection, and recommender-based systems. It involves training or fitting a model on historical data and using the trained model to make predictions for new data.

Spark provides you with the MLlib library which is a RDD based API and ML pipelines which is a DataFrames based API to build machine learning applications. MLlib is in maintenance mode from version 2.0 and it will be deprecated and discontinued in upcoming releases. ML pipelines will be the mainstream API. Apache Mahout was a machine learning library built on top of Hadoop, which is now integrated with Spark to provide in-memory performance and avoid scalability issues. H2O is an open source project that has powerful machine learning and deep learning algorithms that provide integration with Spark as a Sparkling Water product.

Both Hadoop and...