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Big Data Analytics with Hadoop 3
Big Data Analytics with Hadoop 3

Big Data Analytics with Hadoop 3: Build highly effective analytics solutions to gain valuable insight into your big data

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Profile Icon Sridhar Alla
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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (1 Ratings)
Paperback May 2018 482 pages 1st Edition
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Arrow left icon
Profile Icon Sridhar Alla
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Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (1 Ratings)
Paperback May 2018 482 pages 1st Edition
eBook
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Paperback
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Big Data Analytics with Hadoop 3

Overview of Big Data Analytics

In this chapter, we will talk about big data analytics, starting with a general point of view and then taking a deep dive into some common technologies used to gain insights into data. This chapter introduces the reader to the process of examining large data sets to uncover patterns in data, generating reports, and gathering valuable insights. We will particularly focus on the seven Vs of big data. We will also learn about data analysis and big data; we will see the challenges that big data provides and how they are dealt with in distributed computing, and look at approaches using Hive and Tableau to showcase the most commonly used technologies.

In a nutshell, the following topics will be covered throughout this chapter:

  • Introduction to data analytics
  • Introduction to big data
  • Distributed computing using Apache Hadoop
  • MapReduce framework
  • Hive
  • Apache...

Introduction to data analytics

Data analytics is the process of applying qualitative and quantitative techniques when examining data, with the goal of providing valuable insights. Using various techniques and concepts, data analytics can provide the means to explore the data exploratory data analysis (EDA) as well as draw conclusions about the data confirmatory data analysis (CDA). The EDA and CDA are fundamental concepts of data analytics, and it is important to understand the differences between the two.

EDA involves the methodologies, tools, and techniques used to explore data with the intention of finding patterns in the data and relationships between various elements of the data. CDA involves the methodologies, tools, and techniques used to provide an insight or conclusion for a specific question, based on hypothesis and statistical techniques, or simple observation of the...

Introduction to big data

Twitter, Facebook, Amazon, Verizon, Macy's, and Whole Foods are all companies that run their business using data analytics and base many of the decisions on the analytics. Think about what kind of data they are collecting, how much data they might be collecting, and then how they might be using the data.

Let's look at the grocery store example seen earlier; what if the store starts expanding its business to set up hundreds of stores? Naturally, the sales transactions will have to be collected and stored at a scale hundreds of times more than the single store. But then, no business works independently any more. There is a lot of information out there, starting from local news, tweets, Yelp reviews, customer complaints, survey activities, competition from other stores, the changing demographics or economy of the local area, and so on. All such...

Distributed computing using Apache Hadoop

We are surrounded by devices such as the smart refrigerator, smart watch, phone, tablet, laptops, kiosks at the airport, ATMs dispensing cash to you, and many many more, with the help of which we are now able to do things that were unimaginable just a few years ago. We are so used to applications such as Instagram, Snapchat, Gmail, Facebook, Twitter, and Pinterest that it is next to impossible to go a day without access to such applications. Today, cloud computing has introduced us to the following concepts:

  • Infrastructure as a Service
  • Platform as a Service
  • Software as a Service

Behind the scenes is the world of highly scalable distributed computing, which makes it possible to store and process Petabytes (PB) of data:

  • 1 EB = 1024 PB (50 million Blu-ray movies)
  • 1 PB = 1024 TB (50,000 Blu-ray movies)
  • 1 TB = 1024 GB (50 Blu-ray movies...

The MapReduce framework

MapReduce is a framework used to compute a large amount of data in a Hadoop cluster. MapReduce uses YARN to schedule the mappers and reducers as tasks, using the containers. 

An example of a MapReduce job to count frequencies of words is shown in the following diagram:

MapReduce works closely with YARN to plan the job and the various tasks in the job, requests computing resources from the cluster manager (resource manager), schedules the execution of the tasks on the compute resources of the cluster, and then executes the plan of execution. Using MapReduce, you can read write many different types of files of varying formats and perform very complex computations in a distributed manner. We will see more of this in the next chapter on MapReduce frameworks.

Hive

Hive provides a SQL layer abstraction over the MapReduce framework with several optimizations. This is needed because of the complexity of writing code using the MapReduce framework. For example, a simple count of the records in a specific file takes at least a few dozen lines of code, which is not productive to anyone. Hive abstracts the MapReduce code by encapsulating the logic from the SQL statement into a MapReduce framework code, which is automatically generated and executed on the backend. This saves incredible amounts of time for anyone who needs to spend more time on doing something useful with the data, rather than going through the boiler plate coding for every single task that needs to be executed and every single computation that's desired as part of your job:

Hive is not designed for online transaction processing and does not offer real-time queries and...

Apache Spark

Apache Spark is a unified distributed computing engine across different workloads and platforms. Spark can connect to different platforms and process different data workloads using a variety of paradigms such as Spark Streaming, Spark ML, Spark SQL, and Spark Graphx.

Apache Spark is a fast in-memory data processing engine with elegant and expressive development APIs, which allow data workers to efficiently execute streaming machine learning or SQL workloads that require fast interactive access to data sets. 

Additional libraries built on top of the core allow the workloads for streaming, SQL, graph processing, and machine learning. SparkML, for instance, is designed for data science and its abstraction makes data science easier.

Spark provides real-time streaming, queries, machine learning, and graph processing. Before Apache Spark, we had to use different technologies...

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

Introduction to data analytics


Data analytics is the process of applying qualitative and quantitative techniques when examining data, with the goal of providing valuable insights. Using various techniques and concepts, data analytics can provide the means to explore the data exploratory data analysis (EDA) as well as draw conclusions about the data confirmatory data analysis (CDA). The EDA and CDA are fundamental concepts of data analytics, and it is important to understand the differences between the two.

EDA involves the methodologies, tools, and techniques used to explore data with the intention of finding patterns in the data and relationships between various elements of the data. CDA involves the methodologies, tools, and techniques used to provide an insight or conclusion for a specific question, based on hypothesis and statistical techniques, or simple observation of the data.

Inside the data analytics process

Once data is deemed ready, it can be analyzed and explored by data scientists...

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Key benefits

  • Learn Hadoop 3 to build effective big data analytics solutions on-premise and on cloud
  • Integrate Hadoop with other big data tools such as R, Python, Apache Spark, and Apache Flink
  • Exploit big data using Hadoop 3 with real-world examples

Description

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.

Who is this book for?

Big Data Analytics with Hadoop 3 is for you if you are looking to build high-performance analytics solutions for your enterprise or business using Hadoop 3’s powerful features, or you’re new to big data analytics. A basic understanding of the Java programming language is required.

What you will learn

  • Explore the new features of Hadoop 3 along with HDFS, YARN, and MapReduce
  • Get well-versed with the analytical capabilities of Hadoop ecosystem using practical examples
  • Integrate Hadoop with R and Python for more efficient big data processing
  • Learn to use Hadoop with Apache Spark and Apache Flink for real-time data analytics
  • Set up a Hadoop cluster on AWS cloud
  • Perform big data analytics on AWS using Elastic Map Reduce

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Publication date : May 31, 2018
Length: 482 pages
Edition : 1st
Language : English
ISBN-13 : 9781788628846
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Product Details

Publication date : May 31, 2018
Length: 482 pages
Edition : 1st
Language : English
ISBN-13 : 9781788628846
Vendor :
Apache
Category :
Languages :
Concepts :
Tools :

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Table of Contents

12 Chapters
Introduction to Hadoop Chevron down icon Chevron up icon
Overview of Big Data Analytics Chevron down icon Chevron up icon
Big Data Processing with MapReduce Chevron down icon Chevron up icon
Scientific Computing and Big Data Analysis with Python and Hadoop Chevron down icon Chevron up icon
Statistical Big Data Computing with R and Hadoop Chevron down icon Chevron up icon
Batch Analytics with Apache Spark Chevron down icon Chevron up icon
Real-Time Analytics with Apache Spark Chevron down icon Chevron up icon
Batch Analytics with Apache Flink Chevron down icon Chevron up icon
Stream Processing with Apache Flink Chevron down icon Chevron up icon
Visualizing Big Data Chevron down icon Chevron up icon
Introduction to Cloud Computing Chevron down icon Chevron up icon
Using Amazon Web Services Chevron down icon Chevron up icon

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Robert Dal Degan Apr 30, 2020
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The book is light in detail. Don’t buy if you’re a beginner, plenty of vague explanations.
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