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Apache Hadoop REVIEW

Data Analytics Software

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OUR SCORE 9.8

What is Apache Hadoop?

Apache Hadoop is an open source software library and framework designed for the collection, storage, and analysis of large amounts of data sets. It is a reliable and highly-scalable computing technology which can process large data sets across servers, clusters of computers, and thousands of machines in a distributed manner.

Apache Hadoop’s architecture is comprised of core components which include a distributed file system known as HDFS or Hadoop Distributed File System and a programming paradigm and processing component called Map/Reduce. The distributed file system stores data files across machines by dividing them into large blocks. After it splits the files into blocks, it distributes them across the nodes in the cluster of servers or computers.

Meanwhile, Map/Reduce provides a framework built based on the Apache Hadoop YARN system, a technology that handles cluster resource management and job scheduling tasks for applications that are running in a Hadoop cluster. This means Map/Reduce utilizes the capabilities of the Apache Hadoop YARN system to allocate computational resources such as CPUs and memory across and schedule tasks that need to be executed on various cluster nodes.

Overview of Apache Hadoop Benefits

Handle Explosions In Data With Big Data Technology

Apache Hadoop is a big data technology which means it offers an ecosystem, framework, and technology built to process large amounts of data. As companies and organizations evolve and grow, they also have to deal with explosions in data. These are situations or instances wherein they need to process and manage large data sets, and meet the challenges associated with a technological world which is becoming  more information-driven.

Highly-Scalable Framework That Ensures High-Availability

This big data technology is a highly-scalable solution. Apache Hadoop can automatically scale up as the number of servers and machines required to process, store, and analyze large data sets expands. What’s great about this is that the  computing technology eliminates reliance on hardware whenever it needs to scale up. It distributes large data sets across clusters of servers and machines and handles intensive parallel computing on those clusters. In case, errors or failures happen within  each cluster of servers or computers, Apache Hadoop can immediately detect them and provides ways to remediate the issues to ensure high-availability.

Reliable Distributed File System

Apache Hadoop delivers a distributed file system known as HDFS or Hadoop Distributed File System. How does this file system work? The HDFS splits large data files into blocks that are arranged sequentially. Once it’s done dividing the data files into blocks, it distributes and stores the blocks across large clusters of servers or machines. One noteworthy characteristic of this file system is that it is very reliable. The HDFS has a fault tolerance capability, an attribute or property that allows a system to maintain continuous operation despite experiencing failures or faults within its components. It can replicate the blocks of data files it stored and distributed across the clusters; so that in the event that failures occur, tasks and processes can still be executed on the data sets in their replicates.

A Distributed Parallel Computing Component Built Based On Apache YARN

Aside from its reliable distributed file system, Apache Hadoop also has a main component called Map/Reduce. This a framework that utilizes the Apache YARN system to handle distributed parallel computing across Hadoop clusters. Apache YARN system is a cluster management and job scheduling tool which is also developed by The Apache Software Foundation.

Understanding The Map/Reduce Architecture

To understand the reliable and powerful features of Map/Reduce, let us examine its architecture. Map/Reduce comes with an architecture that uses a master/slave structure. Computation operations or tasks are first organized in a single master server called jobtracker. These computation operations or tasks are also referred to as map/reduce jobs. The jobtracker allows users to directly interact with the Apache Hadoop framework. It enables them to send map/reduce jobs to this master server. Then, the jobtracker puts the submitted jobs in queue of pending map/reduce jobs. The jobtracker executes these jobs, prioritizing the execution of the jobs that were submitted earlier – first-come/first-served basis.

The jobtracker assigns the map/reduce jobs to several slave servers known as tasktrackers. Each node in the cluster of servers or computers is linked to a single tasktracker. The tasktrackers are the ones responsible for executing computation operations and tasks on the data sets distributed across the nodes contained in the cluster of servers or computers. However, the manner they execute such operations or tasks depends on the instructions they are getting from the master server or the jobtracker. When the tasktrackers detect failures while they are running computation operations or tasks on the nodes they are assigned to, it redistribute the tasks across other available nodes that are functioning and working properly. In other words, they have the capability to perform good load balancing and can re-execute map/reduce tasks without requiring large runtime overhead.

Overview of Apache Hadoop Features

  • Distributed Processing of Large Data Sets
  • Eliminates Reliance on Hardware to Deliver High-Availability
  • Scalability
  • Can Scale Up From Single Servers to Thousands of Machines
  • Reliable Distributed File Systems
  • Divides Large Data Files into Sequential Blocks
  • Distributes Blocks of Files Across Clusters
  • Fault-Tolerance Capability that Replicates Blocks of Files
  • Map/Reduce Distributed Parallel Computing Framework
  • Utilizes the Cluster Management and Job Scheduling Features of Apache YARN
  • Master/Slave Architecture
  • Re-Distribution and Re-Execution of Computation Operations/Tasks

Apache Hadoop Position In Our Categories

Position of Apache Hadoop in our main categories:

3

Apache Hadoop is one of the top 3 Data Analytics Softwareproducts

3

Apache Hadoop is one of the 3
Data Analytics Software products


If you are interested in Apache Hadoop it could also be a good idea to analyze other subcategories of Best Data Analytics Software collected in our base of SaaS software reviews.

Since each organization has specific business requirements, it is prudent for them to desist from seeking a one-size-fits-all faultless software application. Needless to say, it would be useless to try to find such an app even among popular software solutions. The clever thing to do would be to jot down the various essential elements that require consideration such as required features, budget, skill levels of workers, company size etc. Then, you should do your homework thoroughly. Read some Apache Hadoop reviews and look into each of the other systems in your shortlist in detail. Such in-depth homework can make certain you reject ill-fitting applications and choose the app that offers all the aspects you require for business success.

How Much Does Apache Hadoop Cost?

Apache Hadoop Pricing Plans:
Free trial
Apache Hadoop
Free

Apache Hadoop is delivered based on the Apache License, a free and liberal software license that allows you to use, modify, and share any Apache software product for personal, research, production, commercial, or open source development purposes for free. Thus, you can use Apache Hadoop with no enterprise pricing plan to worry about.

User Satisfaction

We realize that when you make a decision to buy Data Analytics Software it’s important not only to see how experts evaluate it in their reviews, but also to find out if the real people and companies that buy it are actually satisfied with the product. That’s why we’ve created our behavior-based Customer Satisfaction Algorithm™ that gathers customer reviews, comments and Apache Hadoop reviews across a wide range of social media sites. The data is then presented in an easy to digest form showing how many people had positive and negative experience with Apache Hadoop. With that information at hand you should be equipped to make an informed buying decision that you won’t regret.

POSITIVE SOCIAL MENTIONS

265

NEGATIVE SOCIAL MENTIONS

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Technical details

Devices Supported
  • Windows
  • Linux
  • Mac
  • Web-based
Language Support
  • English
  • Chinese
  • German
  • Hindi
  • Japanese
  • Spanish
  • French
  • Russian
  • Italian
  • Dutch
  • Portugese
  • Polish
  • Turkish
  • Swedish
Pricing Model
  • Free
Customer Types
  • Small Business
  • Large Enterprises
  • Medium Business
Deployment
  • Cloud Hosted
  • Open API

What Support Does This Vendor Offer?

  • EMAIL
  • PHONE
  • LIVE SUPPORT
  • TRAINING
  • TICKETS

What are Apache Hadoop pricing details?

Apache Hadoop Pricing Plans:
Free trial
Apache Hadoop
Free

Apache Hadoop is delivered based on the Apache License, a free and liberal software license that allows you to use, modify, and share any Apache software product for personal, research, production, commercial, or open source development purposes for free. Thus, you can use Apache Hadoop with no enterprise pricing plan to worry about.

What integrations are available for Apache Hadoop?

Apache Hadoop integrates with the following open source projects and solutions from The Apache Software Foundation and third-party file systems:

  • Ambari
  • Avro
  • Cassandra
  • Chukwa
  • HBase
  • Hive
  • Mahout
  • Pig
  • Spark
  • Tez
  • ZooKeeper
  • YARN
  • Amazon S3
  • Azure Blob Storage
  • OpenStack Swift

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