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

Analytics with DataFrames


Let's learn how to create and use DataFrames for Big Data Analytics. For easy understanding and a quick example, the pyspark shell is to be used for the code in this chapter. The data needed for exercises used in this chapter can be found at https://github.com/apache/spark/tree/master/examples/src/main/resources. You can always create multiple data formats by reading one type of data file. For example, once you read .json file, you can write data in parquet, ORC, or other formats.

Note

All programs in this chapter are executed on CDH 5.8 VM except the programs in the DataFrame based Spark-on-HBase connector section, which are executed on HDP2.5. For other environments, file paths might change, but the concepts are the same in any environment.

Creating SparkSession

In Spark versions 1.6 and below, the entry point into all relational functionality in Spark is the SQLContext class. To create SQLContext in an application, we need to create a SparkContext and wrap SQLContext...