What Is The Differences between SQL and HiveQL?

What are the differences between SQL and HiveQL? SQL is more general-purpose and associated with traditional relational databases, whereas HiveQL is specialized for querying and managing big data in distributed storage systems like Hadoop.

SQL and HiveQL are programming language that helps to improve the design of the database. Some people tend to use them alternating but they are quite different. The lesson provides the core differences between SQL and HiveQl in tabular form for easy understanding. Let’s find out:

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Differences between SQL and HiveQL (With Table)

Basic Terms SQL HiveQL
View Updatable Read-only
Subqueries Used in any clause Used in FROM, WHERE, or HAVING clauses
Transactions Supported Limited support supported
Indexes Supported Supported
Updates Update, Delete Insert No Update, Delete Insert
Data Types Integral, floating-point, fixed-point, text and binary strings, temporal Boolean, integral, floating-point, fixed-point, text and binary strings, temporal, array, map, struct
Functions Hundreds of built-in functions A limited number of built-in functions
Operation for Structured data and is for RDBMS Structured data
Schema Data storage Data insertion
Usage When we need frequent modification in records. To query large data sets and analyze historical data
Performance Better performance Best performance

What Is SQL?

Structured Query Language, commonly known as SQL, is a standard programming language specifically designed for managing, manipulating, and querying relational databases. It serves as a fundamental tool for interacting with structured data stored in database management systems (DBMS). SQL provides a declarative syntax that allows users to express queries in a clear and concise manner, making it accessible to both beginners and experienced database professionals.

SQL is equipped with a comprehensive set of commands and statements for performing various operations on relational databases. These operations include creating and modifying database schemas, inserting, updating, and deleting records, and retrieving data based on specified criteria. The language supports the definition of relationships between tables, ensuring the integrity and consistency of the stored data.

One of SQL’s core strengths is its versatility, as it is used across a wide range of relational database systems, such as MySQL, PostgreSQL, Oracle, Microsoft SQL Server, and others. This standardization allows users to apply their SQL skills across different database platforms with minimal adaptation.

The structure of SQL queries typically involves key clauses like SELECT (for retrieving data), FROM (specifying the data source), WHERE (filtering data based on conditions), JOIN (combining data from multiple tables), and GROUP BY (grouping results based on specified criteria), among others.

SQL plays a crucial role in data management, providing a powerful and efficient means for organizations to organize, retrieve, and manipulate structured data. Whether used for simple data retrieval or complex database administration tasks, SQL remains a foundational language for working with relational databases in various domains, including business, finance, healthcare, and more.


HiveQL, or Hive Query Language, is a query language that serves as an integral component of the Apache Hive data warehousing and SQL-like querying system. Designed to work within the Hadoop ecosystem, HiveQL provides a high-level, SQL-like interface for interacting with large-scale datasets stored in distributed environments, particularly in Hadoop Distributed File System (HDFS).

HiveQL allows users to define and manipulate structured data using familiar SQL syntax, making it accessible to individuals with a background in relational databases. One of its key features is its ability to abstract the complexities of distributed computing, enabling users to query and analyze data seamlessly across clusters of machines.

Users can perform various operations with HiveQL, including data retrieval, filtering, sorting, and aggregation. The language supports the creation, alteration, and deletion of tables, providing a means to manage the schema of distributed datasets. Additionally, HiveQL facilitates the execution of join operations, enabling the combination of data from multiple sources.

An essential characteristic of HiveQL is its compatibility with Apache Hive, a data warehousing and SQL-like querying infrastructure built on top of Hadoop. This integration allows users to leverage the scalability and fault tolerance of Hadoop for processing and analyzing vast amounts of data.

In summary, HiveQL is a specialized query language tailored for big data analytics within the Hadoop ecosystem. Its SQL-like syntax and integration with Apache Hive make it a powerful tool for managing and querying large-scale datasets in distributed storage environments.

Main Differences between SQL and HiveQL

  1. SQL is for structured data and is for RDBMS while HiveQL is for structured data only.
  2. HiveQL support schema for data insertion while SQL support schema for data storage
  3. SQL is used when we need frequent modification in records whereas HiveQL to query large data sets and analyze historical data.
  4. SQL has hundreds of built-in functions while HiveQL has a limited number of built-in functions.
  5. SQL support updates, delete, and insert while HiveQL does not support any.

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Similarities between SQL and HiveQL

1. Both SQL and HiveQL share a similar syntax structure, utilizing statements like SELECT, FROM, WHERE, and GROUP BY for data retrieval and manipulation.
2. Both languages support the definition and management of database schema, allowing users to create, alter, and drop tables.
3. SQL and HiveQL provide comparable capabilities for filtering data using conditions in the WHERE clause.
4. Both languages support various types of join operations to combine data from multiple tables.
5. Common aggregate functions such as SUM, AVG, COUNT, and MAX are supported in both SQL and HiveQL for summarizing data.
6. Both languages allow users to sort query results based on specified columns using the ORDER BY clause.
7. SQL and HiveQL support the use of subqueries, enabling the nesting of one query inside another to achieve more complex operations.
8. Users can insert, update, and delete records in tables using similar syntax in both SQL and HiveQL.
9. Both languages provide security mechanisms to control access to databases, tables, and specific operations.
10. SQL and HiveQL offer transactional capabilities to ensure the consistency and integrity of data during complex operations.


In conclusion, understanding the differences between SQL and HiveQL is crucial for navigating the diverse landscape of data management tools. SQL, a widely adopted language, is synonymous with traditional relational databases, offering a standardized approach for working with structured data. On the other hand, HiveQL emerges as a specialized solution within the realm of big data, tailored specifically for Apache Hive and its distributed computing environment.

The main distinction lies in their application domains—SQL excels in managing data within traditional relational database systems, while HiveQL shines in the context of large-scale distributed storage systems such as Hadoop. As businesses increasingly grapple with massive datasets and the complexities of distributed computing, recognizing when to leverage SQL or HiveQL becomes paramount.

Whether dealing with everyday business operations or harnessing the power of big data analytics, a nuanced understanding of these two query languages empowers data professionals to make informed decisions, ensuring optimal performance and efficiency in their data management endeavors.

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