Hadoop ecosystem

Distributed Filesystem Apache HDFS The Hadoop Distributed File System (HDFS) offers a way to store large files across multiple machines. Hadoop and HDFS was derived from Google File System (GFS) paper. Prior to Hadoop 2.0.0, the NameNode was a single point of failure (SPOF) in an HDFS cluster. With Zookeeper the HDFS High Availability feature addresses this problem by providing the option of running two redundant NameNodes in the same cluster in an Active/Passive configuration with a hot standby. Red Hat GlusterFS GlusterFS is a scale-out network-attached storage file system. GlusterFS was developed originally by Gluster, Inc., then by Red Hat, Inc., after their purchase of Gluster in 2011. In June 2012, Red Hat Storage Server was announced as a commercially-supported integration of GlusterFS with Red Hat Enterprise Linux. Gluster File System, known now as Red Hat Storage Server. Quantcast File System QFS QFS is an open-source distributed file system software package for large-scale MapReduce or other batch-processing workloads. It was designed as an alternative to Apache Hadoop’s HDFS, intended to deliver better performance and cost-efficiency for large-scale processing clusters. It is written in C++ and has fixed-footprint memory management. QFS uses Reed-Solomon error correction as method for assuring reliable access to data. Reed–Solomon coding is very widely used in mass storage systems to correct the burst errors associated with media defects. Rather than storing thr...

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Overview: Apache Hadoop is an open source framework intended to make interaction with big data easier, However, for those who are not acquainted with this technology, one question arises that what is big data ? Big data is a term given to the data sets which can’t be processed in an efficient manner with the help of traditional methodology such as RDBMS. Hadoop has made its place in the industries and companies that need to work on large data sets which are sensitive and needs efficient handling. Hadoop is a framework that enables processing of large data sets which reside in the form of clusters. Being a framework, Hadoop is made up of several modules that are supported by a large ecosystem of technologies. Introduction: Hadoop Ecosystem is a platform or a suite which provides various services to solve the big data problems. It includes Apache projects and various commercial tools and solutions. There are four major elements of Hadoop i.e. HDFS, MapReduce, YARN, and Hadoop Common. Most of the tools or solutions are used to supplement or support these major elements. All these tools work collectively to provide services such as absorption, analysis, storage and maintenance of data etc. Following are the components that collectively form a Hadoop ecosystem: • HDFS: Hadoop Distributed File System • YARN: Yet Another Resource Negotiator • MapReduce: Programming based Data Processing • Spark: In-Memory data processing • PIG, HIVE: Query based processing of data services • HBas...

As a developer, understanding the Hadoop ecosystem can make you very valuable. Companies are leveraging it for more projects each day, and the average Hadoop developer Frameworks Takeaway OK, so you may be feeling a bit overwhelmed at realizing how much is on this list (especially once you noticethat it's not even a complete list, as new frameworks are being developed each day). But the important thing is that you work toward a basic understanding of these frameworks, so that when a new one pops up, you can relate it back to one of the above. By learning the basic frameworks you're building a strong foundation that will accelerate your learning in the Hadoop ecosystem. Want to learn more? Check out these Contributor Thomas Henson is a Senior Software Engineer and Certified ScrumMaster. He has been involved in many projects from building web applications to setting up Hadoop clusters. Thomas’s specialization is with Hortonworks Data Platform and Agile Software Development. Thomas is a proud alumnus of the University of North Alabama where he received his BBA - Computer Information System, and his MBA - Information Systems. He currently resides in north Alabama with his wife and daughter, where he hacks away at running. With your Pluralsight plan, you can: With your 30-day pilot, you can: • Access thousands of videos to develop critical skills • Give up to 50 users access to thousands of video courses • Practice and apply skills with interactive courses and projects • See sk...

Hadoop is an open-source, Java-based framework that is used to share and process big data. An innovative project that opened up big data horizons for many businesses, Hadoop can store terabytes of data inexpensively on commodity servers that run as clusters. There are also cloud options with Hadoop; its distributed FileSystem is designed to enable greater fault tolerance and concurrent processing.This increase in tolerance and processing speed enables larger quantities of data to be processed more quickly, improving the timeliness of data insights and the level of detailed analysis possible. Hadoop rose to the forefront due to its capacity for processing massive amounts of data and recent innovations have made it even more efficient and useful. Hadoop makes it possible to access database content quickly and efficiently while storing petabytes of information — far beyond what capabilities may be available in a company’s internal database. While all of this may seem very technical, there is a practical business side to Hadoop usage. Specifically, Hadoop remains one of the most important tools that you can have as a data professional. A solid understanding of Hadoop will help develop your data science skills and start you down the path to becoming an adept data science professional. What is Hadoop? Hadoop was first created by Apache and was built in the early 2000s from projects designed to respond to the growth of search engines like Yahoo and Google. Developed by Doug Cutti...

Hadoop ecosystem overview Remember that Hadoop is a framework. If Hadoop was a house, it wouldn’t be a very comfortable place to live. It would provide walls, windows, doors, pipes, and wires. The Hadoop ecosystem provides the furnishings that turn the framework into a comfortable home for big data activity that reflects your specific needs and tastes. The Hadoop ecosystem includes both official Apache open source projects and a wide range of commercial tools and solutions. Some of the best-known open source examples include Spark, Hive, Pig, Oozie and Sqoop. Commercial Hadoop offerings are even more diverse and include platforms and packaged distributions from vendors such as Cloudera, Hortonworks, and MapR, plus a variety of tools for specific Hadoop development, production, and maintenance tasks. Most of the solutions available in the Hadoop ecosystem are intended to supplement one or two of Hadoop’s four core elements (HDFS, MapReduce, YARN, and Common). However, the commercially available framework solutions provide more comprehensive functionality. The sections below provide a closer look at some of the more prominent components of the Hadoop ecosystem, starting with the Apache projects (This article is part of our Hadoop Guide . Use the right-hand menu to navigate.) Apache open source Hadoop ecosystem elements The Apache Hadoop project actively supports multiple projects intended to extend Hadoop’s capabilities and make it easier to use. There are several top-level ...

Hadoop Ecosystem In this blog, let's understand the Hadoop Ecosystem. It is an essential topic to understand before you start working with Hadoop. This Hadoop ecosystem blog will familiarize you with industry-wide used Big Data frameworks, required for a Hadoop certification. The Hadoop Ecosystem is neither a programming language nor a service; it is a platform or framework which solves big data problems. You can consider it as a suite that encompasses a number of services (ingesting, storing, analyzing, and maintaining) inside it. Let us discuss and get a brief idea about how the services work individually and in collaboration. Below are the Hadoop components that, together, form the Hadoop ecosystem. I will be covering each of them in this blog: • HDFS— Hadoop Distributed File System. • YARN— Yet Another Resource Negotiator. • MapReduce— Data processing using programming. • Spark—In-memory Data Processing. • PIG, HIVE — Data Processing Services using Query (SQL-like). • HBase— NoSQL Database. • Mahout, Spark MLlib— Machine Learning. • Apache Drill— SQL on Hadoop. • Zookeeper— Managing Cluster. • Oozie— Job Scheduling. • Flume, Sqoop— Data Ingesting Services. • Solr andLucene— Searching & Indexing. • Ambari— Provision, Monitor and Maintain cluster. Hadoop Ecosystem You may also like: Hadoop Distributed File System • The • HDFSmakes it possible to store different types of large data sets (i.e. structured, unstructured, and semi-structured data). • HDFS creates a level of a...

What is Hadoop Ecosystem? The core Hadoop ecosystem is nothing but the different components that are built on the Hadoop platform directly. However, there are a lot of complex interdependencies between these systems. Before starting this Hadoop ecosystem tutorial, let’s see what we will be learning in this tutorial: • • • • • • • • • • • • • • • There are so many different ways in which you can organize these systems, and that is why you’ll see multiple images of the ecosystem all over the Internet. However, the graphical representation given below seems to be the best representation so far. Watch this Hadoop Video before getting started with this tutorial! MapReduce One interesting application that can be built on top of YARN is MapReduce. MapReduce, the next component of the Hadoop ecosystem, is just a programming model that allows you to process your data across an entire cluster. It basically consists of Mappers and Reducers that are different scripts, which you might write, or different functions you might use when writing a MapReduce program. Mappers have the ability to transform your data in parallel across your computing cluster in a very efficient manner; whereas, Reducers are responsible for aggregating your data together. This may sound like a simple model, but MapReduce is very versatile. Mappers and Reducers put together can be used to solve complex problems. We will talk about MapReduce in one of the upcoming sections of this Hadoop tutorial. Want to learn mo...

In 2002, internet researchers just wanted a better search engine, and preferably one that was open-sourced. That was when Doug Cutting and Mike Cafarella decided to give them what they wanted, and they called their project “Nutch.” Hadoop was originally designed as part of the Nutch infrastructure, and was presented in the year 2005. The Hadoop ecosystem narrowly refers to the different software components available at the MapReduce In the year 2004, Google presented a new Map/Reduce algorithm designed for distributed computation. This evolved into MapReduce, a basic component of Hadoop. It is a Java-based system where the actual data from the HDFS store gets processed. This is a data processing layer designed to handle large amounts of structured and unstructured data. MapReduce breaks down a In the initial “Map” phase, all of the complex logic code is defined. This is a data processing layer designed to process large amounts of structured and unstructured data. In the “Reduce” phase, jobs are separated into small, individual tasks and then managed one at a time. In Hadoop’s ecosystem, MapReduce offers a framework which easily writes applications onto thousands of Hadoop Distributed File System (HDFS) Apache Hadoop’s Users can download huge datasets into the HDFS and process the data with no problems. Apache Hadoop uses a philosophy of hardware failure as the rule, rather than the exception. An HDFS may use hundreds of server machines, with each server storing part of the...