🗃️ Data Model & Schema

HBase: A column-oriented NoSQL database designed for sparse, semi-structured, or unstructured data. It offers a flexible schema, allowing dynamic addition of columns without predefined structures.

RDBMS: Row-oriented systems like MySQL or Oracle use a fixed schema, requiring predefined tables and columns. They are optimized for structured data with strict data types and relationships.

⚙️ Scalability & Architecture

HBase: Built atop Hadoop Distributed File System (HDFS), HBase is horizontally scalable. It can handle massive datasets by distributing them across multiple nodes, making it suitable for big data applications. mindmajix

RDBMS: Typically scale vertically, meaning performance improvements require more powerful hardware. They are less adept at handling very large datasets compared to HBase.

🔄 Transactions & Consistency

HBase: Adheres to the BASE (Basically Available, Soft state, Eventual consistency) model. It doesn't provide full ACID (Atomicity, Consistency, Isolation, Durability) compliance, which may affect transaction integrity in certain applications. GeeksforGeeks

RDBMS: Fully ACID-compliant, ensuring reliable transaction processing with strict consistency and integrity constraints. GeeksforGeeks+1TutorialsPoint+1

🔍 Query Language & Operations

HBase: Lacks a native SQL interface. Data access is primarily through APIs or integration with tools like Apache Hive for SQL-like querying.

RDBMS: Utilize Structured Query Language (SQL) for data manipulation and retrieval, supporting complex queries, joins, and aggregations. GeeksforGeeks

📊 Use Cases & Suitability

HBase: Ideal for applications requiring real-time read/write access to large volumes of sparse data, such as time-series data, logs, and sensor data. ApplyBigAnalytics

RDBMS: Best suited for applications with structured data and complex relationships, like financial systems, customer relationship management (CRM), and enterprise resource planning (ERP) systems.

🧩 Summary Comparison