Cassandra

Azure Cosmos DB is a fully managed, elastically scalable and globally distributed database with a multi-model approach, and provides you with the ability to use document, key-value, wide-column, or graph-based data.

We will drill further into the multi-model capabilities and explore the options that are available to store and access data. Hopefully, it can help you make an informed decision on the right API are the right choice.

• Core (SQL) API: Flexibility of a NoSQL document store combined with the power of SQL for querying.
• MongoDB API: Supports the MongoDB wire protocol so that existing MongoDB client continue to work with Azure Cosmos DB as if they are running against an actual MongoDB database.
• Cassandra API: Supports the Cassandra wire protocol so that existing Apache drivers compliant with CQLv4 continue to work with Azure Cosmos DB as if they are running against an actual Cassandra database.
• Gremlin API: Supports graph data with Apache TinkerPop (a graph computing framework) and the Gremlin query language.
• Table API: Provides premium capabilities for applications written for Azure Table storage.

You can read further on the some of the Key Benefits

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Azure Cosmos DB is a resource governed system that allows you to execute a certain number of operations per second based on the provisioned throughput you have configured. If clients exceed that limit and consume more request units than what was provisioned, it leads to rate limiting of subsequent requests and exceptions being thrown – they are also referred to as 429 errors.

With the help of a practical example, I’ll demonstrate how to incorporate fault-tolerance in your Go applications by handling and retrying operations affected by these rate limiting errors. To help you follow along, the sample application code for this blog is available on GitHub - it uses the gocql driver for Apache Cassandra.