Overview of Apache Hadoop
As Big Data has taken over almost every industry vertical that deals with data, the requirement for effective and efficient tools for processing Big Data is at an all-time high. Hadoop is one such tool that has brought a paradigm shift in this world. Thanks to the robustness that Hadoop brings to the table, users can process Big Data and work around it with ease. The average salary of a Hadoop Administrator which is in the range of US$130,000 is also very promising.
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Apache Hadoop is a Big Data ecosystem consisting of open source components that essentially change the way large datasets are analyzed, stored, transferred and processed. Contrasting to traditional distributed processing systems, Hadoop facilitates multiple kinds of analytic workloads on same datasets at the same time.
Here we have the list of topics if you want to jump right into a specific one:
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Qualities That Make Hadoop Stand out of the Crowd
- Single namespace by HDFS makes content visible across all the nodes
- Easily administered using High Performance Computing (HPC)
- Querying and managing distributed data are done using Hive
- Pig facilitates analyzing the large and complex datasets on Hadoop
- HDFS is designed specially to give high throughput instead of low latency.
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What is Apache Hadoop?
Apache Hadoop is an open-source data platform or framework developed in Java, dedicated to store and analyze the large sets of unstructured data.
With the data exploding from digital mediums, the world is getting flooded with cutting-edge big data technologies. However, Apache Hadoop was the first one which caught this wave of innovation.
Comparison of Hadoop 1 and Hadoop 2 Architectures
While Hadoop is the foundation for most of the big data structures, its different versions came up with varied improvisations. It is alwaysbetter to have a good grasp about the functionalities offered by the successor versions of any technology. Let’s find out the same for Hadoop 1 and Hadoop 2:
| Hadoop 1 | Hadoop 2 |
| Components are- HDFS (V1), MapReduce (V1) | Components are- HDFS (V2), YARN (MR V2), MapReduce (V2) |
| Only one namespace | Multiple namespaces |
| Only one programming model | Multiple programming models |
| Has fixed-sized slots | Has variable sizes of containers |
| Supports maximum of 4,000 nodes per cluster | Supports maximum of 10,000 nodes per cluster |
The most widely and frequently used framework to manage massive data across a number of computing platforms and servers in every industry, Hadoop is rocketing ahead in enterprises. It lets organizations store files that are bigger than what you can store on a specific node or server. More importantly, Hadoop is not just a storage platform, it is one of the most optimized and efficient computational frameworks for big data analytics. The right Hadoop course helps you understand the real world scenarios of working with Big Data.
This Hadoop tutorial is an excellent guide for students and professionals to gain expertise in Hadoop technology and its related components. With the aim of serving larger audiences worldwide, the tutorial is designed for Hadoop Developers, Administrators, Analysts and Testers on this most commonly applied Big Data framework. Right from Installation to application benefits to future scope, the tutorial provides explanatory aspects of how learners can make the most efficient use of Hadoop and its ecosystem. It also gives insights into many of Hadoop libraries and packages that are not known to many Big data Analysts and Architects.
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Together with, several significant and advanced big data platforms like MapReduce, YARN, HBase, Impala, ETL Connectivity, Multi-Node Cluster setup, advanced Oozie, advanced Flume, advanced Hue and Zookeeper are also explained extensively via real-time examples and scenarios, in this learning package.
For many such outstanding technological-serving benefits, Hadoop adoption is expediting. Since the number of business organizations embracing Hadoop technology to contest on data analytics, increase customer traffic and improve overall business operations is growing at a rapid rate, the respective number of jobs and demand for expert Hadoop Professionals is increasing at an ever-faster pace. More and more number of individuals are looking forward to mastering their Hadoop skills through Hadoop online training that could prepare them for various Cloudera Hadoop Certifications like CCAH and CCDH. Get to know more about Your Career in Big Data and Hadoop that can help you grow in your career.
If you find this tutorial helpful, we would suggest you browse through our Big Data Hadoop training.After finishing this tutorial, you can see yourself moderately proficient in Hadoop ecosystem and related mechanisms. You could then better know about the concepts so much so that you can confidently explain them to peer groups and will give quality answers to many of Hadoop questions asked by seniors or experts. If your will to preparing for Big Data Hadoop job, go through this Top Big Data Hadoop Interview Questions And Answers.
Recommended Audience
- Intellipaat’s Hadoop tutorial is designed for Programming Developers and System Administrators
- Project Managers eager to learn new techniques of maintaining large datasets
- Experienced working professionals aiming to become Big Data Analysts
- Mainframe Professionals, Architects & Testing Professionals
- Entry-level programmers and working professionals in Java, Python, C++, eager to learn the latest Big Data technology.
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Prerequisites
- Before starting with this Hadoop tutorial, it is advised to have prior programming language experience in Java and Linux Operating system.
- Basic command knowledge of UNIX and SQL Scripting can be beneficial to better understand the Big data concepts in Hadoop applications.
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Table of Contents
Big Data Overview
Introduction
Big data is a term defined for data sets that are large or complex that traditional data processing applications are inadequate. Big Data basically consists of analysis zing, capturing the data, data creation, searching, sharing, storage capacity, transfer, visualization, and querying and information privacy. What is Big Data? Big Data is a collection of large datasets that cannot be adequately processed Read More
Big Data Solutions
Differentiation between Operational vs. Analytical Systems
Operational Analytical Latency 1 ms to 100 ms 1 min to 100 min Concurrency 1000 to100,000 1 to 10 Access Pattern Writes and Reads Reads Queries Selective Unselective Data Scope Operational Retrospective End User Customer Data Scientist Technology NoSQL Database MapReduce, MPP Database Traditional Enterprise Approach This approach of enterprise will use a computer Read More
Introduction to Hadoop
What is Apache Hadoop?
Apache Hadoop was born to enhance the usage and solve major issues of big data. The web media was generating loads of information on a daily basis, and it was becoming very difficult to manage the data of around one billion pages of content. In order of revolutionary, Google invented a new methodology of processing data Read More
Hadoop Installation
Hadoop Installation Prerequisites
Hadoop is supported by Linux platform and its facilities. So install a Linux OS for setting up Hadoop environment. If you own an operating system than Linux then you can install virtual machine and have Linux inside the virtual machine. Hadoop is written in Java programming, so there exists the necessity of Java installed on the machine and version should be Read More
HDFS Overview
Hadoop Ecosystem
Introduction to Hadoop Distributed File System Hadoop File System was mainly developed for using distributed file system design. It is highly fault tolerant and holds huge amount of data sets and provides ease of access. The files are stored across multiple machines in a systematic order. These stored files are stored to eliminate all possible data losses in case of Read More
HDFS Operations
Starting HDFS Format the configured HDFS file system and then open the namenode (HDFS server) and execute the following command. $ hadoop namenode -format Start the distributed file system and follow the command listed below to start the namenode as well as the data nodes in cluster. $ start-dfs.sh Watch this video on Hadoop by Intellipaat: [videothumb class="col-md-12" id="m5qL78lcXag" alt="Hadoop Read More
MapReduce and Yarn
Introduction to MapReduce
Mapreduce is mainly a data processing component of Hadoop. It is a programming model for processing large number of data sets. It contains the task of data processing and distributes the particular tasks across the nodes. It consists of two phases – Map Reduce Watch this video on Hadoop before going further on this Hadoop tutorial [videothumb class="col-md-12" Read More
Multi-Node Cluster
Setting Up A Multi Node Cluster In Hadoop
Installing Java Syntax of java version command $ java -version Following output is presented. java version "1.7.0_71" Java(TM) SE Runtime Environment (build 1.7.0_71-b13) Java HotSpot(TM) Client VM (build 25.0-b02, mixed mode) [videothumb class="col-md-12" id="RDD6NSCayso" alt="Hadoop Projects" title="Hadoop Projects"] Creating User Account System user account on both master and slave systems should Read More
Streaming
Introduction to Streaming in Hadoop
It uses UNIX standard streams as the interface between Hadoop and your program so you can write Mapreduce program in any language which can write to standard output and read standard input. Hadoop offers a lot of methods to help non-Java development. The primary mechanisms are Hadoop Pipes which gives a native C++ interface to Read More
Apache Pig
Introduction to Apache Pig
Pig raises the level of abstraction for processing large amount of datasets. It is a fundamental platform for analyzing large amount of data sets which consists of a high level language for expressing data analysis programs. It is an open source platform developed by yahoo. Watch this video on Hadoop before going further on this Hadoop tutorial Read More
Apache Hive
What is Hive?
Pig and Hive are open source platform mainly used for same purpose. These tools that ease the complexity of writing difficult/complexed programs of java based MapReduce. Hive is like a data warehouse that uses the MapReduce for the purpose of analyzing data stored on HDFS. It provides a query language called HiveQL that is familiar to the Read More
HBase
Architecture of HBase Cluster
HBase: The Hadoop Database It is an open source platform and is horizontally scalable. It is the database which distributed based on the column oriented. It is built on top most of the Hadoop file system. It is based on the non relational database system (NoSQL). HBase is truly and faithful, open source implementation devised on Google’s Bigtable. Watch this video Read More
Sqoop and Impala
Sqoop Sqoop is an automated set of volume data transfer tool which allows to simple import, export of data from structured based data which stores NoSql systems, relational databases and enterprise data warehouses to Hadoop ecosystems. Watch this video on Hadoop before going further on this Hadoop tutorial [videothumb class="col-md-12" id="qskfdqsK9fk" alt="Hadoop Training for Beginners" title="Hadoop Training for Beginners"] Key features Read More
Oozie and Flume
Oozie It runs both as a server and a client which submits a workflow to the server directly. This workflow based on a DAG of action nodes and control flow nodes. An action node executes a workflow task similar as moving files in HDFS, running a MapReduce job or running a Pig job. A control-flow node handles the complete workflow Read More
Zookeeper and Hue
Zookeeper It allows the distribution of processes to organize with each other through a shared hierarchical name space of data registers. Zookeeper Service is replicated or duplicated over a set of machines. All machines save a copy of the data in memory set. A leader is chosen based on the service startup Clients is only connected to a single Zookeeper Read More
Hive cheat sheet
Introduction:
All the industries deal with the Big data that is large amount of data and Hive is a tool that is used for analysis of this Big Data. Apache Hive is a tool where the data is stored for analysis and querying. This cheat sheet guides you through the basic concepts and commands required to start with it This Read More
PIG Basics Cheat Sheet
Pig Basics User Handbook
Are you a developer looking for a high-level scripting language to work on Hadoop? If yes, then you must take Apache Pig into your consideration. This Pig cheat sheet is designed for the one who has already started learning about the scripting languages like SQL and using Pig as a tool, then this sheet will be Read More
PIG Built-in Functions Cheat Sheet
Pig Built-in Functions User Handbook
Are you a developer looking for a high-level scripting language to work on Hadoop? If yes, then you must take Apache Pig into your consideration. This Pig cheat sheet is designed for the one who has already started learning about the scripting languages like SQL and using Pig as a tool, then this sheet will Read More
good stuff
Excellent Stuff!! Keep it up. I am a hadoop developer. I want to enhance my Hadoop skills therefore I am looking to work on some real – time projects. Willl you please suggest me good platform to work on real-time projects
Thanks Khusboo for your Appreciation. I will suggest you a good source which will definitely enhance your Hadoop Skills https://intellipaat.com/big-data-hadoop-training/
this very helpful to know about basic hadoop concepts. and i found its really helpful to my institute students. keep sharing more.
Very nice informative Hadoop tutorial.Thanks for sharing such a great content to my vision.
Great tutorial. This is covering Hadoop & its Ecosystem, Map Reduce,HDFS,Yarn,Pig,Hive with the examples. Thanks a lot.
With this Hadoop tutorial, I got to know the enough knowledge on Hadoop. Thanks a lot.
Thanks for sharing the proper explanation with this tutorial. It’s useful to learn basic fundamentals of Hadoop.
In hadoop where does the data get stored ?
Hadoop is a highly scalable analytics platform for processing large volumes of structured and unstructured data. By large scale, we mean multiple petabytes of data spread across hundreds or thousands of physical storage servers or nodes.
Really Good tutorial for the Beginners