Data types: Traditional databases typically use a structured data model where data is stored in tables, while Hive, based on Hadoop, is a data warehouse tool that can handle semi-structured and unstructured data.
Query Language: Traditional databases use SQL language to query and operate data, while Hive uses a similar SQL-like language called HiveQL, which enables complex data processing and analysis on Hadoop.
Scalability: Traditional databases usually require vertical scaling on hardware, while Hive can achieve horizontal scaling by adding more nodes to process large-scale datasets.
Processing capability: Hive is suitable for processing and analyzing large data sets, capable of handling data at the petabyte level, while traditional databases may encounter performance bottlenecks when dealing with massive amounts of data.
Data processing methods: Traditional databases typically use OLTP (online transaction processing) mode for handling real-time transaction data, while Hive is commonly used for OLAP (online analytical processing) mode for conducting data analysis and mining.
