Here are key aspects and features of Azure Cosmos DB:

1. Global Distribution:

   • Azure Cosmos DB allows you to distribute data across multiple Azure regions globally, ensuring low-latency access for users and applications regardless of their geographic location.

2. Multi-Model Support:

   • It supports multiple data models, including document (JSON), key-value, graph (Gremlin), and column-family (Cassandra). You can use the model that best fits your application’s requirements.

3. Auto-Scaling:

   • Cosmos DB provides automatic scaling of throughput and storage, allowing your application to handle variable workloads without manual intervention.

4. Multi-API Support:

   • Azure Cosmos DB supports multiple APIs, including SQL, MongoDB, Gremlin, Cassandra, and Table, allowing you to use the API that matches your existing application stack or preference.

5. Consistency Models:

   • It offers multiple consistency models, including strong, bounded staleness, session, and eventual consistency, giving you control over data consistency based on application requirements.

6. Low Latency:

   • Cosmos DB is designed for low-latency data access, with single-digit millisecond response times for reads and writes.

7. Automatic Failover:

   • Azure Cosmos DB provides automatic failover and disaster recovery capabilities, ensuring high availability and fault tolerance.

8. Security and Compliance:

   • It includes features for data encryption at rest and in transit, role-based access control (RBAC), and compliance certifications to meet regulatory requirements.

9. Scalable Databases:

   • You can create multiple databases within a Cosmos DB account, each with its own dedicated resources and data models.

10. Global Distribution and Partitioning:

    • Cosmos DB automatically partitions data and replicates it across regions, allowing you to distribute data close to your users and maintain data availability during regional failures.

11. Change Feed:

    • Cosmos DB provides a change feed that allows you to capture and process real-time data changes, making it suitable for building event-driven applications.

12. Analytics Integration:

    • It can be integrated with Azure Synapse Analytics (formerly Azure SQL Data Warehouse) and Azure Databricks for advanced analytics and reporting.

13. Time to Live (TTL):

    • You can configure TTL for documents to automatically delete or expire data after a specified time, which can be useful for managing data retention.