{"id":7132,"date":"2011-07-21T15:23:01","date_gmt":"2011-07-21T15:23:01","guid":{"rendered":"https:\/\/www.techopedia.com\/definition\/apache-hadoop\/"},"modified":"2024-03-14T16:59:32","modified_gmt":"2024-03-14T16:59:32","slug":"apache-hadoop","status":"publish","type":"definition","link":"https:\/\/www.techopedia.com\/definition\/13800\/apache-hadoop","title":{"rendered":"Apache Hadoop"},"content":{"rendered":"

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

Apache Hadoop is an open source<\/a> software framework for running distributed applications<\/a> and storing large amounts of structured<\/a>, semi-structured<\/a>, and unstructured data<\/a> on clusters of inexpensive commodity hardware<\/a>.<\/p>\n

Hadoop is credited with democratizing big data analytics<\/a>. Before Hadoop, processing and storing large amounts of data was a challenging and expensive task that required high-end, proprietary hardware<\/a> and software<\/a>. Hadoop’s open-source framework and its ability to run on commodity hardware made big data analytics more accessible to a wider range of organizations.<\/p>\n

Since Hadoop was first released in 2006, cloud computing<\/a>, as well as containerization<\/a> and microservice architectures<\/a>, have significantly changed the way applications are developed, deployed, and scaled. While Hadoop is now considered a legacy technology for big data<\/a>, the framework still has specific use cases.<\/p>\n

Techopedia Explains the Hadoop Meaning<\/h3>\n

\"Techopedia<\/p>\n

The Hadoop software framework<\/a> was created by Doug Cutting and Mike Cafarella<\/a> and was inspired by how Google processed and stored large amounts of data across distributed computing environments.<\/p>\n

The name “Hadoop” doesn’t stand for anything; Doug Cutting named the framework after his son\u2019s toy elephant<\/a>. The unique, playful name inspired an ecosystem of open-source tools for obtaining actionable insights from large, complex datasets.<\/p>\n

While Hadoop\u2019s role in projects today may be limited because of more advanced frameworks like Apache Spark<\/a>, Hadoop still plays a role in scenarios where organizations have already invested in Hadoop infrastructure and still have specific use cases for using the Hadoop ecosystem.<\/p>\n

Apache Hadoop vs. Apache Spark<\/span><\/h2>\n

Hadoop\u2019s ability to scale horizontally<\/a> and run data processing applications directly within the Hadoop framework made it a cost-effective solution for organizations that had large computational needs but limited budgets.<\/p>\n

It\u2019s important to remember, however, that Hadoop was designed for batch processing<\/a> and not stream processing<\/a>. It reads and writes data to disk between each processing stage. This means it works best with large datasets that can be processed in discrete chunks, rather than continuous data streams.<\/p>\n

While this makes Hadoop ideal for large-scale, long-running operations where immediate results aren’t critical, it also means that the framework may not be the best choice for use cases that require real-time data processing<\/a> and low-latency<\/a> responses.<\/p>\n

In contrast, Apache Spark<\/a> prioritizes in-memory processing<\/a> and keeps intermediate data in random access memory<\/a> (RAM). This has made Spark a more useful tool for streaming analytics<\/a>, real-time predictive analysis<\/a>, and machine learning<\/a> (ML) use cases.<\/p>\n

Hadoop History<\/span><\/h2>\n

Hadoop\u2019s ability to scale horizontally<\/a> and run data processing applications directly within the Hadoop framework made it a cost-effective solution for organizations that had large computational needs but limited budgets. Here’s a brief overview of how Hadoop came to be:<\/p>\n

Early 2000s<\/strong><\/span>2004<\/strong><\/span>2005<\/strong><\/span>2006<\/strong><\/span>2008<\/strong><\/span>2012<\/strong><\/span><\/div>
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Hadoop was inspired by Google’s publications on its MapReduce<\/a> programming model and the Google File System<\/a> (GFS). These papers described Google’s approach to processing and storing large amounts of data across distributed computing environments<\/a>.<\/p>\n

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Doug Cutting and Mike Cafarella started the Nutch project, an open-source web search engine<\/a>. The need to scale Nutch’s data processing capabilities was a primary motivator in the development of Hadoop.<\/p>\n<\/div>\n

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The first implementation of what would become Hadoop was developed within the Nutch project. This included a distributed file system and the MapReduce programming model directly inspired by the Google papers.<\/p>\n<\/div>\n

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Hadoop became a separate project under the Apache Software Foundation<\/a>.<\/p>\n<\/div>\n

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Hadoop 1.0 was released as a platform for distributed data processing, and the open source ecosystem around Hadoop began to expand rapidly as data scientists<\/a> and data engineers<\/a> began to use the software framework<\/a>.<\/p>\n<\/div>\n

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The Hadoop 2.0 release introduced significant improvements, including YARN<\/a> (Yet Another Resource Negotiator). YARN extended Hadoop\u2019s capabilities beyond just batch processing.<\/p>\n

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How Hadoop Works<\/span><\/h2>\n

Hadoop splits files into storage blocks<\/a> and distributes them across commodity nodes<\/a> in a\u00a0computer cluster<\/a>. Each block is replicated across multiple nodes for fault tolerance<\/a>. Processing tasks are divided into small units of work (maps and reduces), which are then executed in parallel across the cluster. Parallel processing<\/a> allows Hadoop to process large volumes of data efficiently and cost-effectively.\u00a0<\/a><\/p>\n

Here are some of the key features of Hadoop<\/strong> that differentiate it from traditional distributed file systems<\/a>:<\/p>\n