Hadoop Benefits That Make It a Good Big Data Framework
By Indeed Editorial Team
Published July 13, 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
The exponential growth of big data has caused organizations to find frameworks that can manage their volume, veracity, and velocity conveniently. Hadoop framework contains features that can support these various big data features without failure or fault. Learning about Hadoop can help you understand its benefits over other databases and data storing and processing frameworks. In this article, we explain why you may learn about the benefits of Hadoop and what it is, outline big data's characteristics that make Hadoop relevant, highlight its modules, explain how it works, and list its benefits.
Why learn about Hadoop benefits?
If you're a data analyst, data scientist, or a professional who works with data, learning about Hadoop benefits can help inform your decision or convince your organization to adopt it. The core benefit of Hadoop is to help organizations and individuals solve the challenge of storing and sorting large volumes of data. It uses distributed storage and parallel processing of computer hardware to provide benefits such as scalability, speed, resilience, and flexibility.
What is Hadoop?
Hadoop, or Apache Hadoop, is an open-source framework for storing, processing, analyzing, and managing a large amount of data. It's open-source because Apache made the original source code available freely to anyone who wishes to modify and redistribute it. As its introduction, there has been wide adoption of Hadoop due to its many beneficial features. It uses distributed storage from clustered computers and parallel processing to analyze datasets ranging from a few gigabytes to petabytes. Apache regularly updates the framework to include more features and better manage data storing and sorting operations.
Characteristics of big data that make Hadoop relevant
Apache designed Hadoop to manage the different attributes of big data, including:
Velocity: This is the speed at which you generate data. You typically require distributed techniques to process high-velocity data, such as social media messages, because of the rate with which you generate them.
Volume: This refers to the size of the data that requires analyzing and processing, which, for big data, are typically larger than terabytes and petabytes. Hadoop makes it easy to analyze this extensive amount of generated data.
Variety: Big data typically consists of structured, unstructured, and semi-structured data you collect from various sources. These multiple data types also require distinct processing capabilities and specific algorithms.
Veracity: This describes the accuracy and quality tests you perform on data you collect from disparate sources. The more qualitative data is, the higher its valuable contribution to the test's overall results.
Hadoop's operations typically contain four synchronized modules, which are:
Hadoop distributed file system (HDFS)
A distributed file system is a combination of data structures and interfaces that work together to manage various file types on a given storage device. HDFS is a file system that stores data across multiple machines with little prior organization. It runs on standard or low-end hardware, is more fault-tolerant than traditional file systems, and provides better throughput. These various features of the HDFS generally make it suitable for applications with large data sets over conventional file systems.
Yet another resource negotiator (YARN)
Yet another resource negotiator primarily helps schedule jobs and tasks. It divides resource management and job monitoring functionalities into daemons, which are a set of Hadoop processes. YARN typically manages and monitors cluster nodes and resource usage. It also supports resource reservation through a resource-reservation component by allowing users to specify a profile of resources and deadlines.
The map-reduce module in Hadoop is a framework that enables you to write applications that process a significant amount of data quickly. It depends on in-parallel large clusters of commodity hardware in a reliable and fault-tolerant manner. The map task typically takes input data for conversion into a data set others can compute in key-value pairs. After the map task converts the data, the framework sorts and puts them into the reduce task, which aggregates the output and produces the desired result.
Hadoop Common is a series of utilities and libraries usable within the Hadoop framework. These libraries are typically from Java programming language and help start the framework. Similar to the other modules, Common assumes that hardware failures may occur, and you may handle them in software by the Hadoop framework.
How does Hadoop work?
Hadoop works through a coherent integration of and synchronization between its four modules. It consists of multiple machines that form a cluster and distribute data in a parallel manner. Rather than use a single storage unit which can affect the speed and scalability of the system, there's a distribution of storage units among each processor or hardware.
Analysts leverage Hadoop's support for various data types to collect and store data of different formats in the cluster by connecting it to a NameNode using a particular API operation. A typical Hadoop cluster contains Master and Worker nodes that share the same network connection and execute the various jobs across the HDFS. Master nodes typically utilize higher quality hardware, including the NameNode and the JobTracker. Worker nodes typically consist of virtual machines that perform the activities of storing and processing tasks based on the specifications of the Master nodes.
Benefits of using Hadoop
By using Hadoop, businesses derive various benefits, including:
Hadoop is highly scalable because it works on a cluster of machines rather than a single machine. It supports both horizontal and vertical scaling. Vertical scaling occurs when you increase the size of the cluster by adding new nodes, which are essentially new computers or hardware. Horizontal scaling is when you increase the size of the system's components, such as the hard disk and RAM.
Hadoop is a highly fault-tolerant and resilient framework. It protects data and application processing by creating two copies of each data block and storing them in various locations across the nodes. If a machine failure occurs and a block goes missing, you can still find your information within the entire cluster.
Because Hadoop is a cluster of numerous machines, it benefits immensely from the shared resources of its various nodes or hardware. It processes data on all blocks simultaneously, providing parallel processing and improving performance. The Map and Reduce functions of Hadoop also filter and sort data by splitting it concurrently across the servers to reduce the time it takes to process a query.
Many data engineers and analysts also prefer Hadoop because of its cost-effectiveness over other data management frameworks and databases. You can typically build a Hadoop cluster using regular commodity or low-end hardware. It's also an open-source framework that you can access freely at any period.
Flexibility refers to Hadoop's support for the three data types and how it integrates and leverages multiple source systems. It stores records of transactions in a structured manner. The framework can also store semi-structured information such as web server and mobile application logs, internet clickstream records, social media posts, and data from the internet of things (IoT). Hadoop's flexibility extends to images, videos, files, and other formats without failure.
Throughput refers to the amount of work Hadoop performs in a unit of time. The framework divides data among blocks to facilitate parallel and independent data processing. That enables it to use the resources of each node and increases the overall throughput. The framework also operates on a locality principle that states that a computer program only requires access to a small percentage of the system's memory during execution. This principle enables Hadoop to reduce network congestion and also improve throughput.
Low network traffic
Hadoop typically splits tasks from users into independent sub-tasks, which it assigns to each node in the cluster. Each node processes a small amount of data because the framework shares that responsibility among every node. That reduces the load on the entire cluster, which can lower network traffic. Low network traffic can enhance fast information retrieval.
Please note that none of the companies, organizations, or institutions mentioned in this article are affiliated with Indeed.
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