OrientDB differs from traditional relational databases in several key ways :

Data Model : Traditional relational databases follow a tabular data model where data is organized into tables with rows and columns. Each table has a predefined schema, and relationships between tables are established using foreign keys. In contrast, OrientDB supports a multi-model approach, allowing developers to choose from document, graph, or object-oriented data models within the same database instance. This flexibility enables OrientDB to handle diverse data structures and relationships more naturally.

Query Language : Relational databases typically use SQL (Structured Query Language) as the primary query language for data manipulation and retrieval. While OrientDB also supports a SQL-like query language called SQL++, it extends SQL to support graph traversal and non-relational data structures, making it more versatile for querying complex and interconnected data.

ACID Transactions : Both OrientDB and traditional relational databases support ACID (Atomicity, Consistency, Isolation, Durability) transactions to ensure data integrity and consistency. However, the implementation of transactions may vary between the two types of databases, especially in distributed environments. OrientDB's support for distributed transactions across multiple nodes is a notable difference compared to some traditional relational databases.

Schema Flexibility : Traditional relational databases enforce a rigid schema where tables must adhere to a predefined structure, making it challenging to accommodate changes in data requirements. In contrast, OrientDB offers more flexibility with schema-less or schema-evolutionary approaches, allowing developers to store and retrieve data without strict schema enforcement. This flexibility simplifies application development and maintenance, especially in agile environments where requirements evolve over time.
Storage Engine : Relational databases typically use row-based storage engines optimized for tabular data structures. OrientDB, on the other hand, employs a hybrid storage engine that can efficiently handle diverse data models, including documents, graphs, and objects. This hybrid approach enables OrientDB to achieve high performance and scalability while supporting various data access patterns.

Graph Database Capabilities : One of the significant differences is OrientDB's native support for graph data structures and algorithms. While relational databases can represent relationships between entities using foreign keys and join operations, OrientDB's graph database capabilities provide more efficient storage and querying of highly interconnected data, making it suitable for applications like social networks, recommendation systems, and network analysis.

Deployment Options : OrientDB offers flexible deployment options, including embedded mode, standalone server, and distributed clusters. While relational databases can also be deployed in various configurations, OrientDB's built-in support for distributed architectures and horizontal scalability distinguishes it as a more versatile solution for modern, cloud-native applications.