**9. Conclusion**

The Hadoop platform was created as a framework for big data analytics. The opensource Hadoop platform offers the analytical tools and computing capacity needed to handle such massive data volumes. The Hadoop Distributed File System (HDFS) and the MapReduce parallel processing engine are the two primary parts of Apache Hadoop. Apache Hadoop has been successfully established as an open source option for distributed systems in the fields of Big Data, cluster, and cloud computing. Scalability, availability, and fault tolerance to a great degree are promised by the master/ slave design. By simply adding existing hardware, it is possible to obtain costeffectively extra memory, increased I/O capacity, and improved performance. A technology called Map-Reduce allows for the concurrent processing of sizable data sets across many nodes in sizable clusters. Map-Reduce at the level of "Distributed data processing" coupled with the database "HBase" can be taken into consideration since the processing and management of data are two things that are naturally in direct connection.

Because they are self-pollinated crops and farmers can keep and recycle grain from past harvests, the competitiveness of oil crop or legume seed markets is limited by the poor rate of return on investments in breeding, seed production, processing, and marketing. One way to do this is to persuade commercial seed companies to invest in seed production of publicly developed varieties, and to work with them and other stakeholders to improve coordination along the value chain in order to provide farmers with the necessary incentives to invest in improved seed and other complementary inputs to increase productivity and improve quality. The major textile firms produce a variety of products, including polyester staple fiber and filament, yarn, greige cloth, and wax prints.
