Working with Apache Hive and Impala

Learning Apache Hive and Apache Impala won’t take long if you’re already familiar with MySQL, SQL Server, or Oracle SQL. The SQL interface is common and the commands and built-in functions are familiar to work with, despite the underlying differences between these technologies.

This blog will explain what RDBMS, Apache Hive, and Impala have in common.

The sub-languages:
In Apache Hive and Impala, RDMBS sub-languages such as data definition language, data manipulation language, data query language, and control language can be used. They were responsible for creating database objects, manipulating data, retrieving data, and securing database objects.

The table structure:
Even though the underlying storage format is different and the data is stored in files, the front-end will display it as a table with rows and columns.

The built-in Functions:
Whether it’s mathematical functions, string functions, type conversion functions, date-time functions, conditional functions, aggregate functions, windowing and analytical functions, Apache Hive and Apache Impala provide built-in functions that are nearly identical to MySQL.

Integrity Constraints:
Similar to RDBMSs, Apache Hive and Apache Impala may apply constraints at the table and row level. Constraints like primary key, foreign key, not null, and default, among others.

Views can be created in both Apache Hive and Apache Impala. A view is a virtual table based on the result-set of a SQL statement if you’re not familiar with them. A view is similar to a table in that it has rows and columns.

In Apache Hive and Impala, joins, performance tuning with indexes, and many other features are identical. So, have fun learning.

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