Have you heard of heard of Hadoop? Developed by Apache, this open-source software framework is used for distributed storage and distributed processing of large data sets on clusters of commodity hardware. Hadoop offers a cost-effective way for IT organizations to store large amounts of data, all without the restrictions of format requirements.
Driving Hadoop is a programming paradigm called MapReduce, which allows for this exceptional and highly efficient data storage. MapReduce actually refers to two distinct technologies: HDFS and YARN, both of which are fundamental components of Hadoop.
If all of that sounds too confusing, here's another way to view Hadoop: it allows companies and organizations to store and process files larger than their device's capacity. Putting it like that, it's easy to see why so many IT organizations are embracing the technology.
“To understand Hadoop, you have to understand two fundamental things about it: how Hadoop stores files, and how it processes data,” explained Forrester analyst Mike Gualtieri in a tutorial video. "Imagine you had a file that was larger than your PC's capacity. You could not store that file, right? Hadoop lets you store files bigger than what can be stored on one particular node or server. So you can store very, very large files. It also lets you store many, many files.”
History of Hadoop
Hadoop has roots dating back to 2003, during which Google published a paper describing some of the framework's fundamental principles in a paper titled Google File System. Soon after, the search engine giant published a second paper titled MapReduce: Simplified Data Processing on Large Clusters, which further elaborated on these principles and how they can facilitate the processing of Big Data on clusters.
This was an era during which search engines were growing exponentially. With this newfound growth came the need came the need for automated search results; thus, spawning the modern-day “web crawlers” while also laying the foundation for new, more efficient data processing and storing technologies, including Apache Hadoop. Previously, search engines like Google and Yahoo created web results manually, with engineers listing the most relevant websites and pages for popular search queries. As more and more people began using the Internet to find information, search engines needed a new, automated solution for delivering results.
In 2006, Yahoo engineer Doug Cutting took the initial steps towards turning Google's vision of Hadoop into a reality by working on the Apache Nutch project. The Nutch project was later split into two separate projects, however: one focusing specifically on web crawlers (Nutch), while a second project focused on distributed processing and storage (Hadoop). The Apache Software Foundation (ASF) now manages and maintains the complex Hadoop framework and ecosystem.
The Hadoop framework features the following modules:
Hadoop Common – libraries, utilities and tools used by other modules.
Hadoop Distributed File System (HDFS) – distributed file system for data storage; encourages fast processing speeds thanks to its high aggregate bandwidth across the cluster.
Hadoop YARN – resource management platform that manages and uses system resources in clusters.
Hadoop MapReduce – a component of Hadoop's MapReduce programming model that's designed specifically for processing large data sets. It works by processing “parallelizable” programs across large datasets via a large number of computer nodes.
So, what benefits does Hadoop offer? Well, there are several reasons why IT organizations are using this technology, one of which is its near-limitless scalability. If you need a larger system , simply add more nodes; it's that easy. This alone is one of the reasons why Hadoop has become a leading system framework among companies and IT organizations, but there are other reasons to embrace the technology.
Hadoop also provides efficient processing power. With its distributed computing model, it's able to process data effectively and efficiently. If an organization's processing requirements increase, it can simply add more nodes to the system. Or if its processing requirements decrease, it can remove nodes from the system.
There are also cost-savings benefits to using Hadoop over other “relational” database management solutions. The problem with relational database management solutions is that it becomes incredibly expensive to scale them with large volumes of data. To overcome this hurdle, some organizations would simply delete raw data from their databases altogether. But conventional wisdom should lead you to believe that it's never a good idea to delete raw data, in which case an alternative storage solution must be sought. Hadoop's scalable architecture allows organizations to store large data while saving big bucks in the process. According to this article published by Itproportal, traditional storage methods cost “thousands to tens of thousands of pounds” per terabyte. In comparison, Hadoop costs just “hundreds of pounds” per terabyte.
Many organizations overlook the fault-tolerance benefits of Hadoop. Each time data is transferred and store to a node on the system, a duplicate copy is sent to other nodes on the same cluster. What does this mean exactly? Well, in the event of a node failure, you can rest assured knowing that your data is safely backed up on another node (or several).
The fault-tolerance benefits of Hadoop don't end there. When a node does down, all of its associated jobs/tasks are redirected to other notes; thus, ensuring the distributed computing system remains functional at all times.
In terms of speed, Hadoop is incredibly fast. The tools is uses to process data are typically located on the servers on which the data is stored. This allows for faster processing speeds, which is essential when handing large volumes of data, structured or unstructured.
Hopefully, this will give you a better understanding of Hadoop and how it works. In short, Hadoop is an open-source framework for the distributed processing and distributed storage of large data sets on computer clusters consisting of many different individual nodes. It allows companies and IT organizations to store and process large volumes of data effectively and efficiently.
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