Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Big Data Analytics with Hadoop 3
  • Toc
  • feedback
Big Data Analytics with Hadoop 3

Big Data Analytics with Hadoop 3

By : Sridhar Alla
3 (1)
close
Big Data Analytics with Hadoop 3

Big Data Analytics with Hadoop 3

3 (1)
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 (13 chapters)
close
close
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to Hadoop
chevron up
Introduction to Hadoop
Hadoop Distributed File System
MapReduce framework
YARN
Other changes
Installing Hadoop 3 
Summary
2
Overview of Big Data Analytics
Overview of Big Data Analytics
Introduction to data analytics
Introduction to big data
Distributed computing using Apache Hadoop
The MapReduce framework
Hive
Apache Spark
Visualization using Tableau
Summary
3
Big Data Processing with MapReduce
Big Data Processing with MapReduce
The MapReduce framework
MapReduce job types
MapReduce patterns
Summary
4
Scientific Computing and Big Data Analysis with Python and Hadoop
Scientific Computing and Big Data Analysis with Python and Hadoop
Installation
Data analysis
Summary
5
Statistical Big Data Computing with R and Hadoop
Statistical Big Data Computing with R and Hadoop
Introduction
Methods of integrating R and Hadoop
Data analytics
Summary
6
Batch Analytics with Apache Spark
Batch Analytics with Apache Spark
SparkSQL and DataFrames
DataFrame APIs and the SQL API
Schema – structure of data
Loading datasets
Saving datasets
Aggregations
Joins
Summary
7
Real-Time Analytics with Apache Spark
Real-Time Analytics with Apache Spark
Streaming
Spark Streaming
fileStream
Transformations
Checkpointing
Driver failure recovery
Interoperability with streaming platforms (Apache Kafka)
Handling event time and late date
Fault-tolerance semantics
Summary
8
Batch Analytics with Apache Flink
Batch Analytics with Apache Flink
Introduction to Apache Flink
Installing Flink
Using the Flink cluster UI
Batch analytics
Summary
9
Stream Processing with Apache Flink
Stream Processing with Apache Flink
Introduction to streaming execution model
Data processing using the DataStream API
Summary
10
Visualizing Big Data
Visualizing Big Data
Introduction
Tableau
Chart types
Using Python to visualize data
Using R to visualize data
Big data visualization tools
Summary
11
Introduction to Cloud Computing
Introduction to Cloud Computing
Concepts and terminology
Goals and benefits
Risks and challenges
Roles and boundaries
Cloud characteristics
Cloud delivery models
Cloud deployment models
Summary
12
Using Amazon Web Services
Using Amazon Web Services
Amazon Elastic Compute Cloud
Launching multiple instances of an AMI
What is AWS Lambda?
Introduction to Amazon S3
Amazon DynamoDB
Amazon Kinesis Data Streams
AWS Glue
Amazon EMR
Summary

Summary

In this chapter, we have discussed the new features in Hadoop 3.x and how it improves the reliability and performance of Hadoop 2.x. We also walked through the installation of a standalone Hadoop cluster on the local machine.

In the next chapter, we will take a peek into the world of big data analytics.

End of Chapter 1
Next Chapter
bookmark search playlist download
font-size

Change the font size

margin-width

Change margin width

day-mode

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Delete Bookmark

Modal Close icon
Are you sure you want to delete it?
Cancel
Yes, Delete