Hive is a distributed, fault-tolerant data warehouse system that enables analytics at a massive scale. It is built on top of Apache Hadoop, which is an open-source framework used to efficiently store and process large datasets. Hive is designed to work quickly on petabytes of data, and it allows users to read, write, and manage large datasets using SQL-like queries. HiveQL is the SQL-like interface used to query large datasets, leveraging Apache Tez or MapReduce. Hive transforms HiveQL queries into MapReduce or Tez jobs that run on Apache Hadoop’s distributed job scheduling framework, Yet Another Resource Negotiator (YARN). Hive stores its database and table metadata in a metastore, which is a database or file backed store that enables easy data abstraction and discovery.

Hive was created to allow non-programmers familiar with SQL to work with petabytes of data, using a SQL-like interface called HiveQL. Traditional relational databases are designed for interactive queries on small to medium datasets and do not process huge datasets well. Hive instead uses batch processing so that it works quickly across a very large distributed database. Hive is frequently used for data warehousing tasks like data encapsulation, ad-hoc queries, and analysis of huge datasets. It is widely used in the big data industry, especially in companies that have adopted the Hadoop ecosystem.

Some key features of Hive include:

• HiveQL: This is the SQL-like interface used to query large datasets, leveraging Apache Tez or MapReduce.
• Metastore: Hive stores its database and table metadata in a metastore, which is a database or file backed store that enables easy data abstraction and discovery.
• Scalability: Hive is designed to work quickly on petabytes of data, and it allows users to read, write, and manage large datasets using SQL-like queries.
• Fault-tolerance: Hive is a distributed, fault-tolerant data warehouse system.
• Integration with Hadoop: Hive is built on top of Apache Hadoop, which is an open-source framework used to efficiently store and process large datasets.

Hive is not a relational database, but rather a data warehouse infrastructure tool to process structured data in Hadoop. HiveQL is used to process structured data using Hive. Hive supports Data Definition Language (DDL), Data Manipulation Language (DML), and User Defined Functions (UDF). Hive includes many features that make it a useful tool for big data...