This is the third blog in our series on Kafka, where we continue to explore the nuances of deploying Kafka for scale. In our previous blogs, Essential Metrics for Kafka Performance Monitoring and Auto-Instrumenting OpenTelemetry for Kafka, we laid the foundation for understanding Kafka’s performance and monitoring aspects. Now, as we explore further into the Kafka ecosystem, we’re here to tackle the common challenges that can arise during deployment and scaling.

Azure Service Bus is a cloud messaging service that transfers information between services running in both the cloud and on-premises. So, it becomes essential to ensure the performance and availability of Service Bus as it might be used in applications and integrations for transferring business-critical messages. To help you with that, we have listed and compared the top Azure Service Bus monitoring tools with their features.

Apache Kafka is an open-source distributed streaming system that has grown in popularity and usage across the technology industry. Originating from LinkedIn and now part of the Apache Software Foundation, Kafka provides a robust and scalable platform. It’s uniquely designed with an architecture that includes both a storage layer and a compute layer.

Cloud computing’s ascent has redefined modern business operations. Azure, among other platforms, offers unparalleled scalability, speed, and resilience. However, this vast potential brings about the challenge of cost management. Although DevOps teams traditionally focus more on deployment and uptime, addressing Azure costs is essential. Here’s why.

Say hello to the latest arrival in the Azure Integration landscape: Azure API Center, and it’s worth noting that it’s currently in its preview stage!

Apache Kafka, born at LinkedIn in 2010, has revolutionized real-time data streaming and has become a staple in many enterprise architectures. As it facilitates seamless processing of vast data volumes in distributed ecosystems, the importance of visibility into its operations has risen substantially. In this blog, we’re setting our sights on the step-by-step deployment of a containerized Kafka cluster, accompanied by a Python application to validate its functionality. The cherry on top?

Azure Functions offers a serverless solution that streamlines the development process, minimizes infrastructure overhead, and results in cost savings. The beauty of this approach is that you no longer need to grapple with server deployment and maintenance; the cloud infrastructure automatically furnishes the essential resources to support your applications.

Microsoft Azure provides a completely managed event routing service called Azure Event Grid. It allows you to respond to events received from various Azure services and external applications and forward them to different Azure services and endpoints. Azure Event Grid provides a unified way to manage events in Azure with event-driven programming. With Event Grids, you can create event-driven applications in a serverless environment, cutting down costs and performance lags.

Multiple Azure subscriptions are typically managed by a Tenant in an enterprise. Each subscription is tailored to a specific product, project, module, or environment. This article addresses the utilization of Serverless360 for the monitoring and managing these diverse Azure subscriptions.

Azure Database is a comprehensive cloud-based service Microsoft offers as part of its Azure cloud computing platform. It provides various database solutions to cater to different application needs, offering scalability, reliability, and performance. Here’s a quick look at Azure Database: Database Types: Azure Database supports various databases, including SQL databases, NoSQL databases, and data warehousing solutions.