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Monitoring consumer lag in Azure Event Hub

Xebia

Why Consumer lag is the most important metric to monitor when working with event streams. However, it is not available as a default metric in Azure Insights. The post Monitoring consumer lag in Azure Event Hub appeared first on Xebia Blog. Want to have this metric available as part of your monitoring solution?

Azure 130
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Monitoring consumer lag in Azure Event Hub

Xebia

Consumer lag is the most important metric to monitor when working with event streams. However, it is not available as a default metric in Azure Insights. Consumer lag refers to the number of events that still need to be processed by the consumers of a stream. However, there are a few events that can cause that number to rise.

Azure 130
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Azure container Apps: The future of Microservices in Azure?

Xebia

Their aim when building Azure Container Apps was to create an opinionated way of deploying containerized workloads to Azure that brings several features that Kubernetes could provide without having to manage a cluster: autoscaling, zero downtime deployments and traffic shaping with control over ingress.

Azure 130
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Introducing Cloud NGFW for Azure — On-Prem to Azure, Seamlessly Secure

Palo Alto Networks

That’s why we are excited to launch Cloud NGFW for Azure to strengthen security for applications running on Microsoft Azure while streamlining network security operations. Every day this technology blocks nearly 5 billion events, analyzes 3.5 Visit the Azure Marketplace to start a free 30-day trial of Cloud NGFW for Azure.

Azure 109
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Mastering Event-Driven Autoscaling in Kubernetes Environments Using KEDA

Dzone - DevOps

This is where Kubernetes-based Event-Driven Autoscaling (KEDA) plays a pivotal role. KEDA is an open-source project that extends Kubernetes capabilities to provide event-driven autoscaling. This makes it an ideal tool for applications that need to scale based on the volume of messages or events they process. What Is KEDA?

Azure 105
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Securing Azure Kubernetes with Falco

InfoWorld

That means it’s considered stable and ready for use in production environments, including Azure. It joins many of the key components of a cloud-native platform including Helm, Envoy, etcd, KEDA, and Cloud Events.

Azure 79
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Read Azure Eventhub data to DataFrame – Python

Perficient

Reading Azure EventHub Data into DataFrame using Python in Databricks Azure EventHubs offer a powerful service for processing large amounts of data. In this guide, we’ll explore how to efficiently read data from Azure EventHub and convert it into a DataFrame using Python in Databricks.

Azure 52