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
Credits
About the Author
Acknowledgement
About the Reviewers
www.PacktPub.com
Preface
Index

Introducing Apache Hadoop


Apache Hadoop is a software framework that enables distributed processing on large clusters with thousands of nodes and petabytes of data. Apache Hadoop clusters can be built using commodity hardware where failure rates are generally high. Hadoop is designed to handle these failures gracefully without user intervention. Also, Hadoop uses the move computation to the data approach, thereby avoiding significant network I/O. Users will be able to develop parallel applications quickly, focusing on business logic rather than doing the heavy lifting of distributing data, distributing code for parallel processing, and handling failures.

Apache Hadoop has mainly four projects: Hadoop Common, Hadoop Distributed File System (HDFS), Yet Another Resource Negotiator (YARN), and MapReduce.

In simple words, HDFS is used to store data, MapReduce is used to process data, and YARN is used to manage the resources (CPU and memory) of the cluster and common utilities that support Hadoop...