Hadoop History
Hadoop, developed by Doug Cutting and Mike Cafarella in 2005, was inspired by Google’s technologies for handling large datasets. Initially created to improve Yahoo’s indexing capabilities, it consists of the Hadoop Distributed File System (HDFS) and the MapReduce programming model. HDFS enables the storage of data across thousands of servers, while MapReduce processes this data in parallel, significantly improving efficiency and scalability. Released as an open-source project under the Apache Foundation in 2006, Hadoop quickly became a fundamental tool for companies needing to store and analyze vast amounts of unstructured data, thereby playing a pivotal role in the emergence and growth of the big data industry.
Hadoop : Components, Functionality, and Challenges in Big Data
The technical explosion of data from digital media has led to the proliferation of modern Big Data technologies worldwide in the system. An open-source framework called Hadoop has emerged as a leading real-world solution for the distributed storage and processing of big data. Nevertheless, Apache Hadoop was the first to demonstrate this wave of innovation. In the era of big data processing, businesses across various industries need to manage and analyze internal large volumes of data efficiently and strategically.
In this article, we’ll explore the significance and overview of Hadoop and its components step-by-step.
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