ABSTRACT
The Apache Hadoop framework is a widely used distributed computing platform that is designed to process and store large datasets in a reliable and scalable manner. With the growing interest in artificial intelligence (AI) and machine learning (ML) applications, Hadoop has become a popular choice for organizations looking to build infrastructure for big data analytics using AI. One of the key features of Hadoop that makes it ideal for AI applications is its ability to handle massive amounts of data. This is made possible through Hadoop’s distributed file system, HDFS, which enables data to be stored across a cluster of commodity hardware. Hadoop’s Map Reduce programming model also allows for the efficient processing of large datasets in parallel across multiple nodes in the cluster. In addition to its core capabilities, Hadoop also offers a range of tools and technologies that can be useful for AI workloads. For example, Apache Spark provides a powerful data processing engine that can be used for batch processing, streaming, ML, and graph processing. Apache Hive offers an SQL-like interface for querying data stored in Hadoop, while Apache HBase provides a scalable and distributed NoSQL database that can be used for real-time access to data. Overall, the Apache Hadoop framework is a powerful tool for big data analytics using AI, providing a reliable and scalable platform for storing and processing large datasets. With the right expertise and resources, organizations can leverage Hadoop to gain valuable insights and drive innovation in their AI applications.
