Table 3392. Comparison between RDBMS (Relational Database Management Systems) and Apache Hive.
| |
RDBMS |
Apache Hive |
| Data Storage |
Stores data in a structured format using tables. It supports ACID properties (Atomicity, Consistency, Isolation, Durability) to ensure reliable transactions and data integrity. |
Built on top of Hadoop and designed for managing and querying large datasets residing in distributed storage. Hive uses tables, but it stores data in Hadoop's HDFS (Hadoop Distributed File System). |
| Query Language |
Uses SQL (Structured Query Language) for manipulating and querying data. SQL in RDBMS is typically more advanced with support for complex queries and operations. |
Uses HiveQL, which is similar to SQL but tailored for batch processing over large datasets. While HiveQL supports most SQL features, it lacks some advanced SQL functionalities like complex joins and sub-queries. |
| Performance |
Generally faster for transactional processing and complex queries involving small to medium-sized datasets. Well-optimized for operations requiring quick data retrieval. |
Optimized for batch processing of large datasets and not suitable for real-time queries or transaction processing. Performance can be slower compared to traditional RDBMS due to the overhead of running on top of Hadoop. |
| Maximum data size |
The maximum size that RDBMS can handle is terabytes. |
The maximum size Hive can handle is petabytes. |
| Scalability |
Scaling can be challenging and often expensive as it usually requires scaling up with more powerful hardware. |
Highly scalable as it runs on Hadoop’s infrastructure. It is designed to handle petabytes of data by scaling out across a large number of cheaper, commodity servers. |
| Transaction Support |
Fully supports transactions with ACID properties, making it suitable for applications that require reliable data manipulation and consistency. |
Initially, it did not support transactions, but newer versions have started to include some support for ACID transactions, though it’s still not as comprehensive as in traditional RDBMS. |
| Use Case |
Ideal for online transaction processing systems (OLTP), banking systems, CRM, and any other system requiring immediate and reliable transaction processing. |
Best suited for data warehousing solutions, large scale log processing, data analysis, and systems where data is written once and read many times. |
| Data Integrity |
Provides strong data integrity tools like foreign keys, constraints, and triggers to maintain data accuracy and consistency. |
Lacks comprehensive constraints and data validation tools offered by traditional RDBMS, as it is primarily designed for data analysis rather than data integrity. |
| User Tools and Ecosystem |
Generally has a rich set of tools and a mature ecosystem for database management, performance tuning, and security. |
Also has a growing ecosystem but primarily focused on integration with other Hadoop-related technologies and big data tools. |
