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


Users of Spark have three different APIs to interact with distributed collections of data: the RDD API, the DataFrames API, and the new Dataset API. Traditional RDD APIs provide type safety and powerful lambda functions but not optimized performance. The Dataset API and the DataFrame API provide easier ways to work with domain-specific language and provide superior performance over RDDs. The Dataset API combines both RDDs and DataFrames. Users have a choice to work with RDDs, DataFrames, or Datasets depending on their needs. But, in general, DataFrame or Dataset are preferred over conventional RDDs for better performance. Spark SQL uses a catalyst optimizer under the hood to provide optimization.

Dataset/DataFrame APIs provide optimization, speed, automatic schema discovery, multiple sources support, multiple language support, and predicate pushdown; moreover, they are interoperable with RDDs and Datasets. The Dataset API was introduced in version 1.6, which is available in Scala...