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 real-time processing


Big Data is generally ingested in real-time and the value of Big Data must be extracted on its arrival to make business decisions in real-time or near real-time, for example, fraud detection in financial transaction streams to accept or reject a transaction.

But, what is real-time and near real-time processing? The meaning of real-time or near real-time can vary from business to business and there is no standard definition for this. According to me, real-time means processing at the speed of a business. For a financial institution doing fraud detection, real-time means milliseconds for them. For a retail company doing click-stream analytics, real-time means seconds.

There are really only two paradigms for data processing: batch and real-time. Batch processing applications fundamentally provide high-latency, while real-time applications provide low latency. So, processing a few terabytes of data all at once will not be finished in a second. Real-time processing...