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

Using SparkR with Zeppelin

The latest Hortonworks Sandbox provides a preconfigured Zeppelin service, which can be used to work with SparkR scripts. For other virtual machines such as Cloudera or MapR, we need to manually install and configure Zeppelin. Follow the steps created in the The manual method section under the Installing Apache Zeppelin section in Chapter 6, Notebooks and Dataflows with Spark and Hadoop.

Open the Zeppelin UI at http://localhost:9999. Create a new notebook and enter the following SparkR code in a paragraph. In the next paragraph, query the data using SQL. DataFrames returned from SparkR will be displayed using Zeppelin's built-in interactive visualizations, as shown in the following charts (bar plot and pie chart).

If you get an error such as interpreter not found, click on the Interpreter binding icon in the top-right corner of the notebook, and then click on Save to resolve the issue:

cars <- createDataFrame(mtcars)