Cassandra vs OrientDB: What are the differences?

## Introduction
In this markdown, we will discuss the key differences between Cassandra and OrientDB to provide insights into their unique features and functionalities.

1. **Data Model**: Cassandra follows a wide-column model, known for its schema-less design and horizontal scaling capabilities, making it suitable for high write and read workloads, especially in distributed environments. On the other hand, OrientDB is a multi-model database, supporting document, graph, object, and key/value models, allowing users to choose the most appropriate data model for their use case.

2. **Query Language**: Cassandra uses CQL (Cassandra Query Language), a SQL-like language for querying and data manipulation, which simplifies database interactions for users familiar with SQL. In contrast, OrientDB utilizes SQL-like language but with extensions to support graph operations, making it efficient for traversing and querying graph data.

3. **Consistency Mechanism**: Cassandra offers tunable consistency levels, providing users with the flexibility to choose between strong or eventual consistency based on their application requirements. On the contrary, OrientDB employs automatic sharding and distributed ACID transactions to ensure data consistency across distributed nodes.

4. **Scalability**: Cassandra is known for its linear horizontal scalability, allowing it to handle massive amounts of data and requests by adding more nodes to the cluster seamlessly. OrientDB also supports horizontal scalability but requires more manual intervention in data distribution across nodes compared to Cassandra.

5. **Primary Use Cases**: Cassandra is commonly used for high-velocity time-series data, messaging apps, recommendation engines, and logging due to its ability to handle large volumes of data with high write throughput. In comparison, OrientDB is preferred for use cases requiring complex relationships and graph traversal like social networks, fraud detection, and network analysis.

6. **Indexing Mechanism**: Cassandra utilizes a built-in secondary indexing mechanism but lacks full-text search capabilities, making it less suitable for search-intensive applications. In contrast, OrientDB provides versatile indexing options, including full-text search, spatial indexes, and automatic graph indexes, making it suitable for applications requiring extensive search capabilities.

In Summary, the key differences between Cassandra and OrientDB lie in their data models, query languages, consistency mechanisms, scalability approaches, primary use cases, and indexing mechanisms, each catering to distinct application needs and user preferences.