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 on Spark and Hadoop

MLlib is a machine learning library on top of Spark that provides major machine learning algorithms and utilities. It is divided into two separate packages:

  • spark.mllib: This is the original machine learning API built on top of Resilient Distributed Datasets (RDD). As of Spark 2.0, this RDD-based API is in maintenance mode and is expected to be deprecated and removed in upcoming releases of Spark.

  • This is the primary machine learning API built on top of DataFrames to construct machine learning pipelines and optimizations. is preferred over spark.mllib because it is based on the DataFrames API that provides higher performance and flexibility.

Apache Mahout was a general machine learning library on top of Hadoop. Mahout started out primarily as a Java MapReduce package to run machine learning algorithms. As machine learning algorithms are iterative in nature, MapReduce had major performance and scalability issues. So, Mahout stopped...