Hadoop Beginner's Guide Garry Turkington has 14 years of industry experience, most of which has been focused .. Using MySQL tools and manual import. Hadoop Related Books. Contribute to Larry3z/HadoopRelatedBooks development by creating an account on GitHub. He is the author of Hadoop Beginners Guide, published by Packt Publishing in , and is a committer on the Apache Samza project. I would like to thank my.
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Pig can operate on complex data structures, even those that can have levels of nesting. Generally the input data is in the form of file or suited to process the unstructured data.
The input file is passed to the mapper function line by line. PigLatin is relationally complete like SQL, which means it The mapper processes the data and creates several small is at least as powerful as a relational algebra. Turing chunks of data. This stage is the combination of the memory model, and looping constructs.
PigLatin is not Shuffle stage and the Reduce stage. HBase Other components of Hadoop: It was designed to store structured data in tables that could have Hive many of rows and many of columns. Apache is structured and queried in distributed Hadoop.
Hive is also HBase is distributed column based database like layer built a popular development environment that is used to write on Hadoop designed to support billions of messages per day, queries for data in the Hadoop environment. Hive is a HBase is massively scalable and delivers fast random writes declarative language that is used to develop applications for as well as random and streaming reads.
From a data model perspective, utilize its functions. It cannot efficiently perform in small column-orientation gives extreme flexibility in storing data data environments. HBase is ideal for workloads that are write-intensive, need to maintain a large amount of Java is one of the most widely used programming data, large indices, and maintain the flexibility to scale out languages. It has also been connected to various community quickly. Hadoop is one such framework that is Advantages and disadvantages of Hadoop: Therefore, the platform is vulnerable Advantages: The data collected from various sources will be of structured 1 site: The sources can be social media or To build site's product search indices; process millions even email conversations.
A lot of time would need to be of sessions daily for analytics, using both the Java and allotted in order to convert all the collected data into a single streaming APIs; clusters vary from 1 to nodes. Hadoop saves this time as it can derive valuable data from any form of data.
It also has a variety of functions such 2 Yahoo! Hadoop; biggest cluster: In some cases they had to delete large sets of raw data in order to make space for new 3 Facebook: There was a possibility of losing valuable information To store copies of internal log and dimension data sources in such cases.
It is a cost-effective solution for data learning; machine cluster with 2, cores and about storage purposes. Hadoop enables the company to do just that with Managing the data is the big issue. And now days the huge its data storage needs. It uses a storage system wherein the amount of data is produced in the origination so the big data data is stored on a distributed file system.
It is data set that can manage and 4 Multiple copies: For managing the data the big data technique is used i. Hadoop can handle the huge Hadoop automatically duplicates the data that is stored in it amount of data, it is very cost effective, and it can handle and creates multiple copies. This is done to ensure that in huge amount of data so processing speed is very fast,and case there is a failure, data is not lost.
Hadoop-solution to the big data: Figure 2. Actual working of hadoop The above approach is worked fine with that application which processing the small amount of data. But it is not suitable for the application which is having the large amount of data to process.
To solve this problem the Google invented the technique of Hadoop. In this technique the hadoop uses the mapReduce algorithm, in that it divides the task into the small parts and assigns it to separate computer and collect the result in the Figure 4. Hadoop Architecture dataset. In short, Hadoop is used to develop applications based on the Google File System GFS and that could perform complete statistical analysis on huge provides a distributed file system.
HDFS is able to amounts of data. HDFS holds very large amount of data and provides easier access.
Figure 3. Hive is a technology developed at Facebook that turns Hadoop into a data warehouse complete with a b MapReduce Architecture: dialect of SQL for querying.
Being a SQL dialect, Hive is a declarative language. Unlike Pig, in Hive a schema is required, but you are not limited to only one schema. Hive is a technology for turning the Hadoop into a data warehouse, complete with SQL dialect for querying it. Hive works in terms of tables.
There are two kinds of tables you can create: managed tables whose data is managed by Hive and external tables whose data is managed outside of Hive Figure 6. MapReduce Architecture MapReduce framework is the processing pillar of hadoop. Pig The framework is applied on the huge amount of data divided in part and run parallel. The MapReduce algorithm Pig is a procedural language for developing parallel contains two important tasks, namely Map and Reduce. Map processing applications for large data sets in the Hadoop takes a set of data and converts it into another set of data, environment.
Pig is an alternative to MapReduce, and where individual elements are broken down into tuples automatically generates MapReduce functions. Secondly, reduce task, which takes the Pig Latin, which is a scripting language. Pig translates Pig output from a map as an input and combines those data Latin scripts into MapReduce. Pig consists of a language tuples into a smaller set of tuples. As we discuss the various Hadoop-related software packages used in this book, we will describe the particular requirements for each chapter.
However, you will generally need somewhere to run your Hadoop cluster. In the simplest case, a single Linux-based machine will give you a platform to explore almost all the exercises in this book. We assume you have a recent distribution of Ubuntu, but as long as you have command-line Linux familiarity any modern distribution will suffice.
Some of the examples in later chapters really need multiple machines to see things working, so you will require access to at least four such hosts. Virtual machines are completely acceptable; they're not ideal for production but are fine for learning and exploration.
Since we also explore site Web Services in this book, you can run all the examples on EC2 instances, and we will look at some other more Hadoop-specific uses of AWS throughout the book. AWS services are usable by anyone, but you will need a credit card to sign up!
We assume you are reading this book because you want to know more about Hadoop at a hands-on level; the key audience is those with software development experience but no prior exposure to Hadoop or similar big data technologies.
For developers who want to know how to write MapReduce applications, we assume you are comfortable writing Java programs and are familiar with the Unix command-line interface. We will also show you a few programs in Ruby, but these are usually only to demonstrate language independence, and you don't need to be a Ruby expert.
For architects and system administrators, the book also provides significant value in explaining how Hadoop works, its place in the broader architecture, and how it can be managed operationally. Instructions often need some extra explanation so that they make sense, so they are followed with: This heading explains the working of tasks or instructions that you have just completed.
These are short multiple-choice questions intended to help you test your own understanding.
These set practical challenges and give you ideas for experimenting with what you have learned. You will also find a number of styles of text that distinguish between different kinds of information.
Here are some examples of these styles, and an explanation of their meaning. Code words in text are shown as follows: You may notice that we used the Unix command rm to remove the Drush directory rather than the DOS del command. When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold: Newterms and important words are shown in bold.
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We appreciate your help in protecting our authors, and our ability to bring you valuable content. This book is about Hadoop, an open source framework for large-scale data processing. Before we get into the details of the technology and its use in later chapters, it is important to spend a little time exploring the trends that led to Hadoop's creation and its enormous success.
Hadoop was not created in a vacuum; instead, it exists due to the explosion in the amount of data being created and consumed and a shift that sees this data deluge arrive at small startups and not just huge multinationals. At the same time, other trends have changed how software and systems are deployed, using cloud resources alongside or even in preference to more traditional infrastructures.
This chapter will explore some of these trends and explain in detail the specific problems Hadoop seeks to solve and the drivers that shaped its design. This action might not be possible to undo.
Are you sure you want to continue? Upload Sign In Join. Home Books Technology. Save For Later. Create a List. Hadoop Beginner's Guide by Garry Turkington. Summary In Detail Data is arriving faster than you can process it and the overall volumes keep growing at a rate that keeps you awake at night.
Approach As a Packt Beginner's Guide, the book is packed with clear step-by-step instructions for performing the most useful tasks, getting you up and running quickly, and learning by doing.
Who this book is for This book assumes no existing experience with Hadoop or cloud services. Read on the Scribd mobile app Download the free Scribd mobile app to read anytime, anywhere.
Packt Publishing Released: Feb 22, ISBN: Free Access for Packt account holders Preface What this book covers What you need for this book Who this book is for Conventions Time for action — heading What just happened? Time for action — setting up SSH What just happened? Three modes Time for action — configuring the pseudo-distributed mode What just happened? Configuring the base directory and formatting the filesystem Time for action — changing the base HDFS directory What just happened?
Time for action — formatting the NameNode What just happened?
Starting and using Hadoop Time for action — starting Hadoop What just happened? Time for action — implementing WordCount What just happened? Time for action — building a JAR file What just happened?
Time for action — running WordCount on a local Hadoop cluster What just happened? The pre Apart from the combiner…maybe Why have a combiner? Time for action — WordCount with a combiner What just happened? When you can use the reducer as the combiner Time for action — fixing WordCount to work with a combiner What just happened? Reuse is your friend Pop quiz — MapReduce mechanics Hadoop-specific data types The Writable and WritableComparable interfaces Introducing the wrapper classes Primitive wrapper classes Array wrapper classes Map wrapper classes Time for action — using the Writable wrapper classes What just happened?
Time for action — correlating of sighting duration to UFO shape What just happened? Have a go hero Too many abbreviations Using the Distributed Cache Time for action — using the Distributed Cache to improve location output What just happened? Counters, status, and other output Time for action — creating counters, task states, and writing log output What just happened?
Too much information! Summary 5. Advanced MapReduce Techniques Simple, advanced, and in-between Joins When this is a bad idea Map-side versus reduce-side joins Matching account and sales information Time for action — reduce-side join using MultipleInputs What just happened?
Graph algorithms Graph Graphs and MapReduce — a match made somewhere Representing a graph Time for action — representing the graph What just happened? Overview of the algorithm The mapper The reducer Iterative application Time for action — creating the source code What just happened?
Time for action — the first run What just happened? Time for action — the second run What just happened? Time for action — the third run What just happened?