Hadoop ecosystems are often associated with big data. It is an open-source network suite written in Java, which makes the interaction of big data much easier, thereby being an integral component in data-centric companies. Think of how fast Google presents its search results. With every question or keyword inserted into the Google search engine, thousands of links consisting of data are provided in milliseconds. This is similar to when a user is dealing with a Hadoop ecosystem.
Big data are often present in a system as clusters, requiring an ecosystem such as Hadoop, which consists of multiple elements to help process it efficiently without wasting time. The combination of these elements is regarded as an ecosystem that allows big data to be absorbed, stored, analysed, maintained and much more. The most modern type of ecosystem is Apache Hadoop, which originated in 2002 by Doug Cutting and Mike Cafarella. This article will explain the main components of a Hadoop ecosystem and what it means for a data-centric company to have it.
The Elements of a Hadoop Ecosystem
Hadoop is the foundation which allows developers to create other applications to process big data. This is why a Hadoop ecosystem consists of thirteen elements. Three of the most important elements include:
HDFS - Hadoop Distributed File System
This is the storage unit of the system. It consists of two elements: a name node (a master node) and a data node (a slave node). An HDFS can only have one name node, although multiple data nodes exist. The name node is responsible for storing the metadata. This includes information on where the data nodes are located and the directory. On the other hand, a data node contains the user’s data. One of the best features of HDFS is that it replicates data thrice, which in other words, means that it backs up the company data. Therefore, if something occurs to the machine, the HDFS would have taken the necessary measures to ensure no data is lost.
MapReduce - Data Processing Using Programming
MapReduce is where all the data is processed. At this stage, the processing of large chunks of data is made possible by running inputs simultaneously. Similar to HDFS, MapReduce consists of two nodes: a job tracker (master) and a task tracker (slave). While the former allocates the resources and schedules the job to the latter, the latter node ensures that the job is executed. Meanwhile, the task tracker node consistently updates its status periodically to the Job tracker with the help of a heartbeat message. The system can detect whether the task tracker has completed the task allocated to it through the heartbeat message. Where this does not occur, the system presumes that the task has failed and thus reallocates it to another task tracker that can carry it out. It is also at this stage where data is split into multiple data, whereby individual elements are broken into tuples (mapper function). Afterwards, it carries out a process to find the final output (reducer function).
YARN - Yet Another Resource Negotiator
YARN acts as the resource management unit of the ecosystem. In contrast to HDFS, which can only handle one name node, a YARN can handle many. YARN is the operating system of the ecosystem and includes four components. This includes client, resource management, node manager, and map-reduce application master. The central aim of this module is to manage cluster resources and takes measures to ensure no machine is overloaded with data. Hence, when a data specialist searches for a specific type of data to analyse and assess, the Hadoop YARN will find and allocate it. Thus, the client must first submit their job tracker to the resource manager for processing. The resource manager, therefore, manages all the resources in the cluster. The node manager is responsible for maintaining the metadata information and will monitor the user application, after which the Map-reduce application master will execute the application.
Four Advantages of Hadoop
When a business deals with data streams, a Hadoop system becomes a mandatory technology required in the company. Here are four advantages such an ecosystem provides in carrying out daily operations:
Speed
A Hadoop ecosystem handles simple and complex data. Regardless of how complicated data streams can be, the system can uncomplicate them instantly without wasting time. In HDFS, large-size files are broken into small-size file blocks, which are distributed among the nodes available in a Hadoop cluster. These are then processed parallelly, making the process feed much faster and increasing the performance of a company’s operations. Whether a person requires data that weighs MB or TB, with a Hadoop ecosystem, they can receive it within a few minutes.
Flexibility
Traditionally data could only be stored and processed through a single unit. In other words, only a limited type of data in lesser amounts could be processed. However, with the modern Hadoop ecosystem, there can be various data, ranging from structured (MySQL), unstructured (XML, JSON) and semi-structured (images, videos). The system is also robust enough to handle the enormous amount of data that requires storage and processing. The reason behind this is attributed to the fact that a Hadoop ecosystem has multiple processors instead of one, thereby processing data of different types parallelly. Each of these processors will also have a storage unit, preventing bottlenecks in the network’s overheads. By assessing data in its various forms, a company can use the data received from their social media channels, emails and others to gain hidden insights.
Cost Effective
Traditionally, relational databases require expensive hardware and high-end processes to store and handle big data. Due to the increased cost, most companies removed certain data from their system to gain more space in their hardware. As a result, the predictions or insights provided by it were not always accurate. Introducing Hadoop as an open-source software meant that the technology was free for anyone to use. Moreover, unlike relational databases, companies only needed to implement cheaper commodity hardware. Hence, the Hadoop ecosystem is a cost-effective model for companies today.
Cerexio: Allows Data Specialists to Fully Leverage Corporate Data
As one of the best software solution vendors in Asia and the world, Cerexio’s solutions are all compatible with Hadoop ecosystems. Cerexio, therefore, helps you ensure you can meet industrial and commercial practitioners in the industry. Store, process and model data from various devices and receive valuable insights to make intelligent decisions. Unlock the data-tackling capabilities offered by a typical Apache Hadoop ecosystem and enjoy the other advanced features that come with our digital solutions. They have all equipped with industry 4.0 technologies, including artificial intelligence (AI), machine learning (ML), predictive and prescriptive analytics, digital twin, simulation technology and much more. If you are an SME with futuristic goals, incorporating a scalable digital solution from Cerexio ensures you can easily receive more storage for your corporate data. As it is a license-free software service, this is also a very cost-effective solution in the long term.
Connect with us to learn how the Hadoop ecosystem can help you meet the structural demands of your data specialists.
A Mandatory Tool For Big Data Companies
Big data companies are constantly looking for digital tools to help them process large data sets faster and provide accurate, actionable insights. One such tool that helps with this process is a Hadoop ecosystem. This is an excellent asset for companies that handle loads of data as it specialises in batch processing. Note that a Hadoop system is not perfect in all its forms. However, data companies implementing external measures can prevent any concerns it raises. Hence, regardless of any negative aspects you read online, understand that you need to have a Hadoop ecosystem to handle a large amount of data.
