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Big Data Analytics with Hadoop 3

Big Data Analytics with Hadoop 3

By : Sridhar Alla
3 (1)
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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)
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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
Introduction to Hadoop
Hadoop Distributed File System
MapReduce framework
YARN
Other changes
Installing Hadoop 3 
Summary
2
Overview of Big Data Analytics
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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

Visualization using Tableau

Whichever method we use to perform distributed computing on big data, its very hard to comprehend the meaning of the data without the aid of tools such as Tableau, which can provide an easy to understand visualization of data.

We can do visualization using many tools such as Cognos, Tableau, Zoom data, KineticaDB, Python Matplotlib, R + Shiny, JavaScript, and so on. We will cover visualization in more detail in Chapter 10, Visualizing Big Data.

The following is a simple horizontal bar chart in Tableau:

Figure: Screenshot showing a simple horizontal bar chart in Tableau

The following is a geospatial view of data in Tableau:

Figure: Screenshot of a geospatial view of data in Tableau
End of Section 8
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