The Hadoop ecosystem has helped organizations to save costs working with large datasets. Most Hadoop implementations use commodity hardware for storage and processing. This helps companies build low-cost infrastructures to provide high availability and scalable processing power. However, Hadoop's MapReduce processing model was mostly written in Java. The existing data storage infrastructure was mostly developed on traditional relational databases that uses SQL for data processing. Thus, it is necessary to have a tool that can provide similar functionality in the Hadoop ecosystem.
Hive is a data warehouse tool that can process huge amounts of data stored over a distributed storage system, like HDFS using SQL-like queries. The user uses Hive query language, which is very much similar to other SQL-like languages. Hive was developed with the purpose of easing the job of data warehouse users who have strong knowledge of SQL queries and who find it difficult to adopt Java or other languages...
