The term ‘Serverless’ grabbed the attention of the developers and after the immediate release of serverless, developers started developing serverless applications. The key areas of serverless applications are as follows:
Azure Cosmos DB Trigger: Azure functions offer Triggers capability which allows the users to execute code and respond to the events in a proper manner. The trigger functions now offer CosmosDB support and multi-model database service. Here is the example of the architecture of CosmosDB and how it triggers multiple serverless functions.
Microsoft Graph Binding: Binding function makes communication easy with other services and the support includes a set of Microsoft Graph Binding that enables your functions to communicate with Excel, Outlook, OneDrive, and Microsoft Graph. You can also integrate millions of functions at a time in Microsoft cloud.
Cross-platform Developers Experience: In the past, Azure offers local development experience for functions which they could deploy to the cloud. Now, both MacOS and Linux developers can use this function and all thanks go to the migration of the run-time to cross-platform. This runtime also offers local development experience and developers can utilize their experiences too. For the further assistance, you can see how does cross platform tooling for Azure Functions work.
Monitoring with Azure Applications Insights GA:
Monitoring and collection of the data are the essential traits of a production app. Azure App Insights offers the full set of functions and the preview includes the tracing through ILogger, metrics streaming, and functions proxies executions. Monitoring Azure Functions does provide the complete details of Insight GA.
Integration with Azure Stream Analytics: Azure Stream Analytics is the best solution to the growing data and number of solutions due to the integration of the sensors and massive volumes of telemetry data round the clock. It offers real-time analytics on Azure and allows developers to insert custom logic into their stream analytics pipeline. For the further assistance, you can read Azure Stream Analytics: Output to Azure Functions.