Title: Apache Hive
Author: Kriko
Published: May 24, 2023
Last modified: Jul 7, 2026

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# Apache Hive

**Apache Hive** is an open-source data warehouse system used to query, process and
analyse large datasets. Developed as part of the Apache Hadoop ecosystem, it makes
information stored in distributed environments accessible through SQL-like commands.
It is commonly used for batch processing, reporting and ETL workflows. The project
was initially developed at Facebook and was later transferred to the Apache Software
Foundation.

Apache Hadoop is a technology ecosystem designed to store and process large datasets
across clusters of relatively low-cost commodity servers. The Hadoop Distributed
File System, or HDFS, stores information across multiple machines. MapReduce is 
a programming model that distributes large processing tasks across the cluster. 
The Hadoop ecosystem also includes additional components for resource management
and common system services.

Apache Hive uses a SQL-like query language called **HiveQL**, or HQL. Rather than
being a general-purpose programming language, HiveQL is a declarative query language
used to define, retrieve, filter, group and transform data. Users with SQL experience
can therefore adapt more easily to analysing large datasets through Hive. However,
HiveQL and standard SQL may differ in certain features and syntax.

Hive does not necessarily store the underlying data within its own system. Information
may be held in HDFS, cloud-based object storage services or other compatible distributed
environments. The Hive Metastore stores metadata such as table structures, columns,
data types and file locations. This allows users to query distributed files through
an organised model consisting of tables and columns.

Queries written in HiveQL may be executed through engines such as MapReduce, Apache
Tez or Apache Spark, depending on the selected architecture. Hive converts the user’s
query into tasks that can be processed across a distributed environment. This enables
users to analyse large datasets without writing low-level distributed processing
code. Performance nevertheless depends on factors such as data layout, partitioning,
file format and the selected execution engine.

Apache Hive is generally suitable for batch processing, reporting and transformation
of large volumes of data. It may not be appropriate for operational applications
requiring real-time transactions, frequent record updates or extremely low response
times. Data engineers, analysts, reporting specialists and data scientists can use
Hive to work with distributed information. It can also be integrated with different
storage, processing and analytics technologies across Hadoop and cloud environments.

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