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 web-based notebooks


We have worked with the Spark shell and applications in previous chapters. The shell provides great features, such as trying out code quickly and checking results interactively. However, when code becomes larger, it is difficult to edit some lines and re-execute the code. This is where applications are useful in which the entire script is saved in a file and submitted. However, in this way, you lose powerful Read, Evaluate, Print, and Loop (REPL) features of the shell. Notebooks solve this problem by providing features of both the shell and application in a web browser.

Web-based notebooks are files that contain the input code and output such as results and graphs from an interactive session. They also contain additional information, such as documentation, mathematical expressions, and media related to an interactive session. They are stored in the JSON format and can be shared with anybody across the organization or externally. It is easy to view the existing...