![]() ![]() MongoDB supports nested documents and arrays, which can be more difficult to model in Cosmos SQL. One reason is that MongoDB has a more flexible data model than Cosmos SQL. There are some scenarios where using MongoDB in Cosmos DB might be a better choice. Depending on the amount of data and the complexity of your application, this migration process can be a complex task. If you need to use a different API for your data, you will need to create a new container with the appropriate API and data model, and then migrate your data to the new container. The data stored in that container must conform to the data model and API that was chosen. When you create a container in Cosmos DB, you choose a specific API and data model that is associated with that container. An instance of data is tightly bound to API, and has to be set at the creation time. Similarly, if you create a container using the SQL API, you can only store data in that container using the SQL data model and API. Each container in Cosmos DB is associated with a specific API and data model, and data stored in a container must conform to that data model.įor example, if you create a container using the MongoDB API, you can only store data in that container using the MongoDB data model and API. You cannot use different APIs in the same container in Azure Cosmos DB. This provides flexibility in how you structure and access your data, while still taking advantage of the benefits of a globally distributed, multi-model database service. This means that you can store data using different data models, and access that data using the appropriate API for your application.įor example, you could store structured data in a SQL API container, and unstructured data in a MongoDB API container within the same Cosmos DB account. ![]() Azure Cosmos DB is designed to be a multi-model database service that supports multiple APIs, including SQL, MongoDB, Cassandra, Azure Table, and Gremlin. You can use both SQL and MongoDB APIs in Azure Cosmos DB at the same time. Overall, Cosmos DB provides a comprehensive set of features for building modern, scalable, and highly available applications in the cloud. Cosmos DB provides a range of tools and SDKs to make it easy for developers to get started with the service, including Azure Portal, Azure CLI, Azure PowerShell, and various programming language SDKs. Cosmos DB provides built-in analytics and visualization capabilities with Azure Synapse Link, allowing developers to perform real-time analytics on their data. Cosmos DB provides advanced security features such as encryption at rest and in transit, role-based access control, and virtual network integration. Cosmos DB supports multiple APIs such as SQL, MongoDB, Cassandra, Azure Tables, and Gremlin, making it easy to use with existing applications and tools. Cosmos DB provides a range of consistency levels to choose from, allowing developers to balance between data consistency and availability. This means that as the application grows, Cosmos DB can automatically scale to handle the increased workload. Cosmos DB provides automatic scaling of throughput and storage, based on the needs of the application. This allows applications to provide low latency and high availability for users around the world. Cosmos DB provides global distribution of data across multiple regions and zones, with automatic replication and failover capabilities. Cosmos DB supports multiple data models such as document, key-value, graph, and column-family, allowing developers to use the most appropriate data model for their specific use case. It provides a way to store and query structured data in a distributed environment, and it can be a good choice for certain types of applications. What is Cosmos DB?Ĭosmos SQL is a data model within Cosmos DB that supports SQL-like queries against JSON data. It’s a question I asked myself for a long time, so I’ve decided to answer it for myself and for someone else.
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