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.
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While Hadoop is the foundation for most of the big data structures, its different versions came up with varied improvisations. It is always better 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 training 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.
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.
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
Traditional Enterprise Approach This approach of enterprise will use a computer to store and process big data. For storage purpose is available of their choice of database vendors such as Oracle, IBM, etc. The user interacts with the application, which executes data storage and analysis. Limitation This approach are good for those applications which require low storage, processing and database capabilities, but when Read More
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 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 1.6 or Read More
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
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 Listing Files in HDFS Finding the list of files in a Read More
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 Map converts a typical dataset into another set of data where individual elements Read More
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) Creating User Account System user account on both master and slave systems should be created to use the Hadoop installation. Read More
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
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. [cf7pdfs id="1" type="e" heading="Get Hadoop Certification in just 50 hrs" btntxt="Get Read More
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: 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. Column oriented Read More
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. Key features of Sqoop It has following features: JDBC based implementation are used Auto generation of tedious user side code Integration with hive Extensible Read More
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 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
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
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
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
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