Hive allows users to read, write, and manage petabytes of data using SQL and is position on top of Apache Hadoop, which is an open-source framework used efficiently to store and process large datasets. Apache Spark and it is essential tools for big data and analytics.
It provides functionalities like extraction and analysis of data using SQL-like queries. As a result, and is closely integrate with Hadoop, and is design to work quickly on petabytes of data. HIVE stands for Highly Immersive Visualization Environment.
It is a data warehouse infrastructure software that can create interaction between user and HDFS. The user interfaces that Hive supports are Hive Web UI, Hive command line, and Hive HD Insight. Hive is a data warehouse tool use to process structured data in the Hadoop environment. It is built on top of Hadoop and is primarily use to make querying and analysis easy. Developers use Hive to store schema in a database and store processed data into HDFS and is designed for querying and managing only structured data stored in tables, as it is scalable, fast, and uses familiar concepts. Schema gets store in a database, while processed data goes into a Hadoop Distributed File System.
- Keeps queries running fast.
- Provides the structure on an array of data formats
- HiveQL is a declarative language like SQL.
- Takes very little time to write query in comparison to MapReduce code.
- Very easy to write query including joins.
- Multiple users can query the data with the help of HiveQL.
Topics in Hive :
1. Introduction to Hadoop Hive and important modules
2. Data Encapsulation and data analysis basis
3. Data Transformation and format handling
4. Finding bugs and Deriving useful information
5. Cloud data management and handling
6. Training solutions and Workforce management
7. Rules Validating and security control