{"id":16382,"date":"2023-11-30T23:58:05","date_gmt":"2023-11-30T23:58:05","guid":{"rendered":"https:\/\/businessyield.com\/tech\/?p=16382"},"modified":"2023-12-01T13:46:06","modified_gmt":"2023-12-01T13:46:06","slug":"hadoop","status":"publish","type":"post","link":"https:\/\/businessyield.com\/tech\/technology\/hadoop\/","title":{"rendered":"HADOOP: What Is It & What Is It Used For?","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"

If you deal with big data or simply merge, join, store, or manage big data, chances are that you already use Hadoop, the open-source framework that’s used to store or manage big data. Generally, it’s an invaluable piece of software for big data users. This guide is a breakdown of what it is, what it’s used for, and, of course, its key components and everything you need to know about it. <\/p>

Overview of Hadoop<\/span><\/h2>

Hadoop is an open-source framework for distributed storage and processing of large data sets. It was created by Doug Cutting and Mike Cafarella and named after Cutting’s son’s toy elephant. The project originated at Yahoo! in 2005 as an initiative to build a scalable and cost-effective solution for storing and processing massive amounts of data.<\/p>

According to cloud.google.com<\/a>, the history of Hadoop can be traced back to the early days of the Web. Hadoop gained significant traction in the early 2000s, and its development was accelerated with the formation of the Apache Hadoop project in 2006. The Apache Software Foundation took over the project’s development and turned it into an open-source initiative, allowing a diverse community of developers to contribute to its growth.<\/p>

Over the years, it has become a cornerstone of the big data ecosystem. It has been widely adopted by organizations for its ability to handle large-scale data processing tasks efficiently and cost-effectively. However, as the field of big data evolved, new technologies and frameworks emerged, challenging Hadoop’s dominance in certain use cases. One of the major shifts in the big data landscape was the rise of Apache Spark, an alternative to MapReduce that offers faster and more flexible data processing. Spark’s in-memory processing capabilities and a more developer-friendly API made it a popular choice for many big data applications.<\/p>

In recent years, the Hadoop ecosystem has continued to evolve, adapting to new trends and integrating with other technologies. While the hype around it has somewhat diminished, it remains a critical component in many large-scale data processing environments, and its core concepts have influenced the design of other data processing frameworks.<\/p>

What is Hadoop?<\/span><\/h2>

Hadoop is a distributed storage and processing framework designed for handling large datasets using a cluster of commodity hardware. It is an open-source solution that offers scalability and fault tolerance for efficient data management. The system seamlessly expands its capacity from individual servers to several machines, providing an economical approach to efficiently managing large volumes of data.<\/p>

In clearer terms, it’s a collection of open-source software that leverages a distributed network of numerous computers to address complex problems that entail extensive data processing and computational requirements. The software framework offers a platform for distributed storage and processing of large-scale data using the MapReduce programming model.<\/p>

How Does Haoop Work?<\/span><\/h2>

Hadoop operates using a distributed storage system named HDFS and a distributed processing methodology, MapReduce. This enables the handling and analysis of vast amounts of data across a cluster of computers. The architecture and components of the system collaborate to facilitate the decentralized storage and processing of extensive datasets across a group of workstations. Hadoop’s use of HDFS for distributed storage and MapReduce for distributed processing enables it to effectively manage large data applications by offering scalability, fault tolerance, and the capability to process data in parallel across numerous nodes. <\/p>

Key Components of Hadoop<\/span><\/h3>

Several components make up the basis for the key functionality of Apache Hadoop. The following are some of these components:<\/p>

#1. Hadoop Distributed File System (HDFS)<\/span><\/h3>

HDFS is a distributed file system designed to store vast amounts of data across multiple machines. It breaks down large files into smaller blocks (typically 128 MB or 256 MB) and distributes them across the nodes in a Hadoop cluster.<\/p>

#2. MapReduce<\/span><\/h3>

MapReduce is a programming model and processing engine for parallel and distributed computing of large data sets. It divides tasks into two phases: the map phase, where data is processed and transformed into key-value pairs, and the reduce phase, where the results are aggregated.<\/p>

#3. YARN (Yet Another Resource Negotiator)<\/span><\/h3>

Yet another resource negotiator (YARN) is the resource management layer of Hadoop that enables multiple data processing engines (like MapReduce, Apache Spark, and others) to share resources on the same cluster.<\/p>

#4. Common Utilities<\/span><\/h3>

Common utilities generally include various utilities and libraries that support the Hadoop ecosystem, including tools for distributed data storage, data access, data serialization, and more.<\/p>

What is Hadoop Used For?<\/span><\/h2>

Hadoop is primarily used for distributed storage and processing of large volumes of data. Other uses include:<\/p>