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 allows users to read, write, and manage petabytes of data using SQL-like queries called HiveQL. Hive is designed to work quickly on petabytes of data and is closely integrated with Hadoop. 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 used to analyze structured data and provides the functionality of reading, writing, and managing large datasets residing in distributed storage. Hive supports Data Definition Language (DDL), Data Manipulation Language (DML), and User Defined Functions (UDF) . Hive Metastore provides a central repository of metadata that can easily be analyzed to make informed, data-driven decisions, and therefore it is a critical component of many data lake architectures.
In summary, Hive is a data warehouse system that allows users to read, write, and manage petabytes of data using SQL-like queries called HiveQL. It is built on top of Apache Hadoop and is designed to work quickly on petabytes of data. Hive Metastore provides a central repository of metadata that can easily be analyzed to make informed, data-driven decisions, and therefore it is a critical component of many data lake architectures.
