HBase vs Relational Databases: Key Differences

1. Data model: HBase is a column-oriented database where data is stored in column families and columns can be added dynamically. In contrast, traditional relational databases are row-based, with data stored in tables where each row contains a fixed set of columns.
2. Data storage: HBase is a distributed storage system based on Hadoop, where data is stored on HDFS and supports horizontal scaling. Unlike traditional relational databases which are typically deployed on a single machine or cluster and cannot easily scale like HBase.
3. Data Consistency: HBase implements eventual consistency, which means there may be a delay after data is written before all nodes have consistent data. In contrast, traditional relational databases typically use strong consistency, where data is immediately visible on all nodes in the cluster after being written.
4. Query Language: HBase typically utilizes the HBase API or a SQL-like language similar to Hive for data querying, while traditional relational databases use SQL language.
5. Data Consistency: HBase utilizes Zookeeper for distributed coordination and ensuring consistency, a feature that is not typically needed in traditional relational databases.

In general, HBase is suitable for large-scale data storage and analysis, providing high reliability and horizontal scalability; while traditional relational databases are suitable for transaction processing and complex queries, offering strong consistency and a simple data model.