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

Analytics with the Dataset API

Datasets are similar to RDDs; however, instead of using Java or Kryo Serialization, they use a specialized Encoder to serialize the objects for processing or transmitting over the network. While both encoders and standard serialization are responsible for turning an object into bytes, encoders are generated dynamically and use a format that allows Spark to perform many operations such as filtering, sorting, and hashing without deserializing the bytes back into an object. Source:

Creating Datasets

The following Scala example creates a Dataset and DataFrame from an RDD. Enter the scala shell with the spark-shell command:

scala> case class Dept(dept_id: Int, dept_name: String)
defined class Dept

scala> val deptRDD = sc.makeRDD(Seq(Dept(1,"Sales"),Dept(2,"HR")))
deptRDD: org.apache.spark.rdd.RDD[Dept] = ParallelCollectionRDD[0] at makeRDD at <console>:26

scala> val...