Section 10:105. Overview of SQL

Hive is a data warehousing tool built on top of Hadoop, which allows users to query and analyze data stored in Hadoop Distributed File System (HDFS). Hive provides a SQL-like interface called HiveQL or HQL, which allows users to write SQL queries to analyze data stored in Hadoop.

Here are some key features of SQL in Hive:

  1. HiveQL: HiveQL is the SQL-like language used in Hive. It is a declarative language used to query data stored in HDFS. HiveQL supports most of the SQL standard syntax, including SELECT, FROM, WHERE, GROUP BY, and ORDER BY clauses.
  1. Data Types: Hive supports several data types including Boolean, TinyInt, SmallInt, Int, BigInt, Float, Double, Decimal, String, and Timestamp. In addition, Hive supports complex data types such as arrays, maps, and structs.
  1. Joins: Hive supports several types of joins, including inner join, left join, right join, and full outer join. Users can use the JOIN keyword to join tables based on a common field.
  1. Aggregations: Hive supports several aggregate functions, including SUM, COUNT, AVG, MIN, and MAX. Users can use these functions to aggregate data and get summary information.
  1. Partitioning: Hive supports partitioning of tables based on one or more columns. This allows users to efficiently query large datasets by only scanning a subset of data.
  1. Indexing: Hive supports indexing of tables, which allows users to quickly look up data based on a particular column or set of columns.

Overall, SQL in Hive provides a powerful and flexible interface for analyzing data stored in Hadoop. Its support for complex data types, partitioning, and indexing makes it well-suited for large-scale data warehousing applications.

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