Definition of Apache Hadoop
It 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. Let’s find out what is Hadoop software and Hadoop system. We will learn about the entire Hadoop ecosystem, Hadoop applications, Hadoop Common and Hadoop framework.
- Allows multiple concurrent tasks to run from single to thousands of servers without any delay.
- Consists of a distributed file system that allows transferring data and files in split seconds between different nodes.
- Able to process efficiently even if a node fails
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|Common||Carries the libraries and utilities used by the other modules.|
|HDFS||Allows storing huge data across multiple machines.|
|YARN||Responsible for splitting the functionalities and scheduling the jobs|
|MapReduce||Processes each task into two sequential steps, i.e., map task & reduce task|
How did Apache Hadoop evolve?
Inspired by Google’s MapReduce which splits an application into small fractions to run on different nodes, scientists Doug Cutting and Mike Cafarella created a platform called Hadoop 1.0 and launched it in the year 2006 to support distribution for Nutch search engine.
It was made available for public in November 2012 by Apache Software Foundation. Named after a yellow soft toy elephant of Doug Cutting’s kid, this technology has been continuously revised since its launch.
As part of its revision, it launched its second revised version Hadoop 2.3.0 on 20th Feb, 2014 with some major changes in the architecture.
What comprises of Hadoop Data architecture/ecosystem?
The architecture can be broken down into two branches, i.e., Hadoop core components and complementary/other components.
Core Hadoop Components :
There are four basic or core components :
- Hadoop Common – It is a set of common utilities and libraries which handle other Hadoop modules. It makes sure that the hardware failures are managed by Hadoop cluster automatically.
- HDFS – HDFS is a Hadoop distributed file system that stores that stores data in the form of small memory block and distributes them across the cluster. Each data is replicated multiple times to ensure data availability.
- Hadoop YARN – It allocates resources which in turn allow different users to execute various applications without worrying about the increased workloads.
- Hadoop MapReduce – It executes tasks in a parallel fashion by distributing it as small blocks.
Complementary/other Hadoop components
Ambari – Ambari is a web-based interface for managing, configuring and testing big data clusters to support its components like HDFS, MapReduce, Hive, HCatalog, HBase, ZooKeeper, Oozie, Pig and Sqoop. It provides a console for monitoring the health of the clusters as well as allows assessing the performance of certain components such as MapReduce, Pig, Hive, etc., in a user-friendly way.
Cassandra – An open source highly scalable distributed database system based on NoSQL dedicated to handle massive amount of data across multiple commodity servers, ultimately contributing to high availability without a single failure.
Flume – A distributed and reliable tool to for effectively collecting, aggregating and moving bulk of streaming data into HDFS.
HBase – A non-relational distributed databases running on the Big Data Hadoop cluster that stores large amount of structured data. HBase acts as an input for the MapReduce jobs.
HCatalog – It is a layer of table and storage management which allows the developers to access and share the data.
Hadoop Hive – Hive is a data warehouse infrastructure that allows summarization, querying, and analyzing of data with the help of a query language similar to SQL.
Hadoop Oozie – A server-based system that schedules and manages the Hadoop jobs..
Hadoop Pig– A dedicated high-level platform which is responsible for manipulating the data stored in HDFS with the help of a compiler for Mapreduce and a language called Pig Latin. It allows the analysts to extract, transform and load (ETL) the data without writing the codes for MapReduce.
Solr – A highly scalable search tool which enables indexing, central configuration, failovers and recovery.
Spark – An open source fast engine responsible for Hadoop streaming and supporting SQL, machine learning and processing graphs.
Hadoop Sqoop – A mechanism to transfer huge amount of data between Hadoop and structured databases.
Hadoop Zookeeper – An open source application that configures synchronizes the distributed systems.
How to download Hadoop?
This section you will learn about Hadoop download. To work in the Hadoop environment you need to first download Hadoop which is an Open Source tool. The Hadoop download can be done on any machine for free since the platform is available as an open source tool. But there are certain system requirements that need to be satisfied for successful download of the Hadoop framework viz.
Hadoop can work on any ordinary hardware cluster. All you need is some commodity hardware and you are good to go.
When it comes to operating system Hadoop is able to run on the UNIX and Windows platforms. Linux is the only platform that is used for product requirements.
When it comes to the browser most of the popular browsers are easily supported by Hadoop. These browsers include the Microsoft Internet Explorer, Mozilla Firefox, Google Chrome, Safari for Windows and Macintosh and Linux systems depending on the need.
The software requirement for Hadoop is the Java software since the Hadoop framework is mostly written in Java programming language. The minimum version for Java is the Java 1.6 version.
Within the Hadoop ecosystem the Hive or HCatalog requires a MySQL database for successfully running the Hadoop framework. You can directly run the latest version or let the Apache Ambari decide on the wizard that is required for the same.
“The world is one big data problem.” – Andrew McAfee, Associate Director, MIT
Types of Hadoop installation
There are various ways in which Hadoop can be run. Here are the various scenarios in which Hadoop can be downloaded, installed and run.
Though Hadoop is a distributed platform for working with big data, we can even install Hadoop on a single node in a single standalone instance. This way the entire Hadoop platform runs like a system which is running on Java. This is mostly used for the purpose of debugging. It helps if you want to check your mapreduce applications on a single node before running on a huge cluster of Hadoop.
Fully Distributed mode
This is distributed mode that has several nodes of commodity hardware connected to form the Hadoop cluster. In such a setup the NameNode, JobTracker and SecondaryNameNode work on the master node whereas the datanode and the secondarydatanode work on the slave node. The other set of nodes namely the datanode and the TaskTracker work on the slave node.
This in effect is a single node Java system that runs the entire Hadoop cluster. So the various daemons like the NameNode, DataNode, TaskTracker and JobTracker run on the single instance of the Java machine to form the distributed Hadoop cluster.
There are various components within the Hadoop ecosystem like the Apache Hive, Pig, Sqoop, ZooKeeper. The various tasks of each of these components are different. Hive is a SQL dialect that is primarily used for data summarization, querying and analysis. Pig is a data flow language that is used for abstraction so as to simplify the MapReduce tasks for those who do not know to code in Java for writing MapReduce applications.
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Example of Hadoop – wordcount
The most equivalent example of the Hello World program in the Hadoop domain is the MapReduce program for working with big data. This program consists of the Mapping of the particular occurrences of a certain word in a file. The Map task will map the data and send it across to the reduce side wherein this data is combined to get the final result. This is one of the most important Hadoop examples that is being often cited.
Cloudera offers the most popular platform for distributed Hadoop framework working in an open source framework. Cloudera helps for enterprises to get the most out of the Hadoop framework thanks to its packaging of the Hadoop tool in a much easy to use system. Cloudera is the world’s most popular Hadoop distribution platform. Cloudera is a completely open source framework and has a very good reputation of upholding the Hadoop ethos and it has a history of bringing the best technologies to the public domain like the Apache Spark, Parquet, HBase and more.
You can install the Hadoop in the various types of setup for working as per the needs of the big data processing.
Major Hadoop commands
Hadoop has various file system commands that directly interact with the Hadoop distributed file system in order to get the required results.
- Hadoop infrastructure
These are some of the most common commands that are used in Hadoop for performing various tasks within the Hadoop framework.
What is Hadoop streaming?
Hadoop streaming is the generic API that is used for working with streaming data. Both the Mapper and Reducer obtain their inputs in standard format. The input is taken from the Stdin and the output is sent to Stdout. This is the method within Hadoop to work with continuous stream of Data for processing it on a continuous basis.
Hadoop is the application which is used for big data processing and storing. Hadoop development is the task of computing big data through the use of various programming languages like Java, Scala and others. Hadoop supports a range of data types like Boolean, Char, Array, Decimal, String, Float, Double and so on. Hadoop data analytics is an increasingly important application that is delivered by Hadoop.
“Information is the oil of the 21st century, and analytics is the combustion engine.”–Peter Sondergaard, VP, Gartner
Some of the interesting facts behind the evolution of Big Data Hadoop are:
- The Google File system gave rise to the HDFS
- The MapReduce program was created to parse web pages
- The Google BigTable directly gave rise to HBase
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Why should we use Apache Hadoop?
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With evolving Big Data around the world, the demand for Hadoop developers is increasing at rapid pace. The well-versed Hadoop developers with the knowledge of practical implementation are very much required to add value into existing process. However apart from many other reasons, following are the prime reasons to use this technology:
- Extensive use of Big Data : More and more companies are realizing that in order to cope with the outburst of data, they will have to implement a technology that could subsume such data into itself and come out with something meaningful and valuable. Hadoop has certainly addressed this concern and the companies are tending towards adopting this technology. Moreover a survey conducted by Tableau reports that among 2,200 of customers, about 76% of respondents who are already using Hadoop wish to use it in newer ways.
- Customers expect security : Nowadays security has become one of the major aspects of IT infrastructure. Hence the companies are keenly investing in the security elements more than anything. Apache Sentry, for instance, enables role-based authorization to the data stored in the big data cluster.
- Latest technologies taking charge : The trend of big data is going upward as the users are demanding higher speed and thus are rejecting the old school data warehouses. Realizing the concern of its customers, Hadoop is actively integrating latest technologies like Cloudera Hadoop Impala, AtScale, Actian Vector, Jethro, etc., in its basic infrastructure.
Some of the Hadoop companies who have implemented this open source infrastructure are :
- Facebook-Social Networking Website
- Twitter-Social Networking Website
- LinkedIn-Social Networking Website
- Yahoo-Online Portal
- AOL-Online Portal
- Cloudspace-IT Developer
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What is Apache Hadoop scope?
In the market full of analytical technologies, Hadoop has made its mark and is certainly going to go far ahead in the race. Following facts prove this statement in a clearer way:
(1) As per a research conducted by MarketsandMarkets, the efficiency and reliability of Hadoop has created a buzz among the software biggies. According to its report, the growth of this technology is going to be $13.9 billion by 2017 which is 54.9% higher than its market size in 2012.
(2) Apache Hadoop is in its nascent stage and is only going to grow in its near and long-term future because of two reasons:
- Companies need a distributed database that is capable of storing large amount of unstructured and complex data as well as can process and analyze the data to come up with meaningful insights
- Companies are willing to invest in this area but they need a technology that is upgradable in lesser cost and is comprehensive in many ways.
(3) Marketanalysis.com reports Hadoop’s market to have a grip in following segments between the years 2017 and 2022:
- It is supposed to have a strong impact in Americas, EMEA and Asia Pacific
- It will have its own commercially supported software, own hardware and appliances, consulting, integration and middleware supports.
- It will be applied in large spectrum of areas like Advanced/Predictive analytics, Data Integration/ETL, Visualization/Data Mining, Clickstream analysis & social media, Data Warehouse Offload, Mobile devices & Internet of Things, Active Archive, Cybersecurity log analysis, etc
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Why do we need Hadoop?
The explosion of Big Data has forced the companies to use the technologies that could help them manage the complex and unstructured data in such a way that maximum information could be extracted and analyzed without any loss and delay. This necessity sprouted the development of big data technologies that are able to process multiple operations at once without a failure. Here are some of the features of Hadoop as listed below
- Capable of storing and processing complex datasets : With increasing volumes of data, increases the possibility of data loss and failure. However Hadoop’s ability to store and process large and complex unstructured datasets makes it somewhat special.
- Great computational ability : Its distributed computational model enables fast processing of Big Data with multiple nodes running in parallel.
- Lesser faults: Implementing it leads to lesser number of failures as the jobs are automatically redirected to other nodes as and when one node fails. This ultimately causes the system to respond in real-time without failure.
- No pre-processing required : Enormous data can be stored and retrieved at once, including both structured and unstructured data without having to preprocess before storing into the database.
- Highly scalable : It is a highly scalable big data tool as you can raise the size of cluster from single machine to thousands of servers without having to administer extensively.
- Cost-effective: Open source technologies come free of cost and hence require lesser amount of money for implementing them.
“With data collection, ‘the sooner the better’ is always the best answer.”–Marissa Mayer, Ex. CEO of Yahoo
Who is the right audience to learn Hadoop?
The world is getting inclined towards data analytics and so are the professionals. Therefore Hadoop will definitely act as an anchor for the aspirants who wish to make their career in Big Data analytics. Moreover, it is best suited for software professionals, ETL developers, analytics professionals, etc.
However, a thorough idea of Java, DBMS and Linux will surely give them an upper hand in the domain of analytics.
How Hadoop will help you in career growth?
- Huge demand for skilled professionals
According to a Forbes report of 2015, about 90% of global organizations are investing in big data analytics and about one third of organizational call it “very significant.” Hence, it can be implied that Big Data Hadoop will not only remain merely a technology but a magical wand in the hands of the companies trying to mark their presence in the market. Therefore learning Hadoop is like a feather in the cap of the beginners aspiring to see themselves as the analysts ten years from now.
- More market opportunities
As per the market trends which gives an upward trajectory for Big Data analytics shows that the demand for data scientists and analysts is not going to decline anytime soon. This clearly indicates that learning this technology will give a surety about making a successful career in this industry.
- Big bucks As per the statistics : Hadoop Developer Salary in United States -$102,000
This clearly gives an idea that learning big data technologies will be your sure-fire ticket to grabbing the top-paying jobs in the data analytics world without an iota of doubt.
Hadoop has taken the Big Data market by storm as the companies are constantly getting benefitted by its scalability and reliability. Though it will be exaggerating to say that it is the only player in the market, but the continuous advancements have made it a preferable choice for the companies. With the increasing number of companies gravitating towards big data analytics, learning this technology and being well-versed with its functionality will definitely lead an aspirant to new heights of career.
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