Book Image

Big Data Analytics with Hadoop 3

By : Sridhar Alla
Book Image

Big Data Analytics with Hadoop 3

By: Sridhar Alla

Overview of this book

Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once you have taken a tour of Hadoop 3’s latest features, you will get an overview of HDFS, MapReduce, and YARN, and how they enable faster, more efficient big data processing. You will then move on to learning how to integrate Hadoop with the open source tools, such as Python and R, to analyze and visualize data and perform statistical computing on big data. As you get acquainted with all this, you will explore how to use Hadoop 3 with Apache Spark and Apache Flink for real-time data analytics and stream processing. In addition to this, you will understand how to use Hadoop to build analytics solutions on the cloud and an end-to-end pipeline to perform big data analysis using practical use cases. By the end of this book, you will be well-versed with the analytical capabilities of the Hadoop ecosystem. You will be able to build powerful solutions to perform big data analytics and get insight effortlessly.
Table of Contents (18 chapters)
Title Page
Copyright and Credits
Packt Upsell
Scientific Computing and Big Data Analysis with Python and Hadoop


As it is expected that real-time streaming applications will run for extended periods of time while remaining resilient to failure, Spark Streaming implements a mechanism called checkpointing. This mechanism tracks enough information to be able to recover from any failures. There are two types of data checkpointing:

  • Metadata checkpointing 
  • Data checkpointing

Checkpointing is enabled by calling checkpoint() on the StreamingContext:

def checkpoint(directory: String)

This specifies the directory where the checkpoint data is to be stored. Note that this must be a filesystem that is fault tolerant, such as HDFS.

Once the directory for the checkpoint is set, any DStream can be checkpointed into it, based on an interval. Revisiting the Twitter example, each DStream can be checkpointed every 10 seconds:

val ssc = new StreamingContext(sc, Seconds(5))
val twitterStream = TwitterUtils.createStream(ssc, None)
val wordStream = twitterStream.flatMap(x => x.getText().split(" "))
val aggStream...