大数据专业毕业论文外文译文关于Hadoop的不少于1500个字

Hadoop is an open-source framework for distributed storage and processing of large datasets. It is designed to handle big data, which refers to datasets that are too large and complex to be processed by traditional data processing systems. Hadoop is based on the MapReduce programming model, which allows developers to write programs that can process large datasets in parallel across a large number of computers.

One of the key features of Hadoop is its ability to store and process data across a large number of nodes in a cluster. This allows Hadoop to scale to handle datasets that are too large for a single computer. Hadoop also provides fault tolerance, which means that if a node in the cluster fails, the data and processing can be automatically moved to another node without any loss of data.

Hadoop consists of two main components: Hadoop Distributed File System (HDFS) and MapReduce. HDFS is a distributed file system that is designed to store large datasets across a cluster of computers. MapReduce is a programming model that allows developers to write programs that can process large datasets in parallel across the cluster.

Hadoop has become a popular tool for big data processing and analytics. It is used by companies such as Facebook, Yahoo, and eBay to process and analyze large datasets. Hadoop is also used in scientific research, such as in the analysis of large datasets in the field of genomics.

In addition to its scalability and fault tolerance, Hadoop also provides a number of other features that make it a powerful tool for big data processing. These include support for a variety of data formats, including structured, unstructured, and semi-structured data; support for complex data types, such as arrays and maps; and support for data compression, which can reduce the amount of storage required for large datasets.

Overall, Hadoop is a powerful and flexible tool for big data processing and analytics. Its ability to store and process large datasets across a distributed cluster of computers makes it ideal for handling the challenges of big data. With its growing popularity and adoption, Hadoop is likely to continue to play a key role in the future of big data processing and analytics