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

Input sources and output stores


Spark Streaming supports three kinds of input sources:

  • Basic sources: Sources directly available in the StreamingContext API. Examples: file systems, socket connections, and Akka actors.

  • Advanced sources: Sources like Kafka, Flume, Kinesis, Twitter, and so on, which are available through extra utility classes.

  • Custom sources: Requires implementing a user-defined receiver.

Multiple receivers can be created in the same application to receive data from different sources. It is important to allocate enough resources (cores and memory) for enabling receivers and tasks to execute simultaneously. For example, if you start your application with one core, it will be taken by the receiver and no tasks will be executed because of a lack of available cores.

Basic sources

There are four basic sources available in Spark StreamingContext as shown in the following table:

Source

Description

TCP stream

For streaming data via TCP/IP by specifying a hostname and a port number...