Azure Spring Cloud

It’s possible to build seamless device-to-cloud experience with the integrations between Azure and Confluent Cloud. The example provided in this blog post showcases how the following capabilities of Azure and Confluent Cloud are leveraged:

• A fully managed platform for data in motion and self-managed connectors from Confluent
• A secure connection from Confluent to Azure
• A highly distributed and scalable data management solution from Azure
• A limitless analytics service offered by Azure that brings together data integration, enterprise data warehousing, and big data analytics
• A fully managed microservice development platform on Azure

The following diagram provides high-level architecture of the end-to-end solution:

Self-managing a distributed system like Apache Kafka ®, along with building and operating Kafka connectors, is complex and resource intensive. It requires significant Kafka skills and expertise in the development and operations teams of your organization. Additionally, the higher the volumes of real-time data that you work with, the more challenging it becomes to ensure that all of the infrastructure scales efficiently and runs reliably.

Confluent and Microsoft are working together to make the process of adopting event streaming easier than ever by alleviating the typical infrastructure management needs that often pull developers away from building critical applications. With Azure and Confluent seamlessly integrated, you can collect, store, process event streams in real-time and feed them to multiple Azure data services. The integration helps reduce the burden of managing resources across Azure and Confluent.