1. Data Model: Amazon DynamoDB uses a NoSQL data model, specifically a key-value pair model, where each item is uniquely identified by a primary key. Cassandra, on the other hand, utilizes a column-family data model, which allows for more flexible schema design and faster querying by columns.

2. Consistency Model: DynamoDB offers strong consistency, ensuring that when a read operation is performed, the latest version of an item is always returned. In contrast, Cassandra provides tunable consistency, allowing users to choose their desired level of consistency, trading off performance for data accuracy.

3. Scalability: DynamoDB is fully managed by Amazon Web Services (AWS) and automatically scales horizontally to handle any amount of traffic or data, making it highly scalable. Cassandra also provides scalability through its distributed architecture, but it requires manual configuration and monitoring to ensure efficient scaling.

4. Secondary Indexes: DynamoDB provides built-in support for secondary indexes, allowing users to query data using non-primary key attributes. Cassandra, on the other hand, requires users to design and manage their own secondary indexes, which can be more complex and error-prone.

5. Query Language: DynamoDB uses AWS SDKs or API to interact with the database, whereas Cassandra provides its own query language called CQL (Cassandra Query Language), which is similar to SQL and allows for more expressive and relational-like querying.

6. Durability and Availability: DynamoDB offers automatic data replication and multi-AZ deployments, ensuring high durability and availability. In contrast, Cassandra requires manual configuration for data replication and does not provide built-in multi-AZ support, requiring users to handle replication and fault-tolerance themselves.

In Summary, Amazon DynamoDB and Cassandra differ in their data models, consistency models, scalability approaches, secondary index support, query languages, and durability/availability features.