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


The goal of any recommendation system is to recommend products, such as movies, music, books, news, search queries, and products, to targeted users. Broadly, there are two approaches to build recommendation systems—content-based filtering and collaborative filtering. While content-based filtering is based on item attributes, collaborative filtering is based on users and items.

Spark's MLlib implements a collaborative filtering algorithm called Alternating Least Squares (ALS) to build recommendation systems with explicit feedback or implicit feedback from users. Recommendation systems with lambda architecture can be built using Mahout and Solr, which are used for real-time recommendations.

The next chapter introduces graph analytics with GraphX.