Operations | Monitoring | ITSM | DevOps | Cloud

Though invisible to most users, APIs are the backbone of modern web applications. Developers love them because they facilitate complex integrations between systems and services. The business loves them because integrating disparate systems to create new products and services drives innovation and growth. The challenge with this transformative connectivity is the dependencies that exist between systems. API failure can result in performance degradation, data anomalies, or even system-wide outages.

The rise of public cloud services has enabled businesses to innovate faster, scale effortlessly, and adopt more advanced technologies easier than ever before. However, there’s a dark side to using public cloud services: complexity and cost. Public cloud services can scale to handle almost any workload, but in doing so, they can quickly generate unpredictable costs for your business.

This blog series is focused on observability into Kafka based applications. In the previous blogs, we discussed the key performance metrics to monitor different Kafka components in "Monitoring Kafka Performance with Splunk" and how to collect performance metrics using OpenTelemetry in "Collecting Kafka Performance Metrics with OpenTelemetry." In this blog, we'll cover how to enable distributed tracing for Kafka clients with OpenTelemetry and Splunk APM.

More and more, we see our clients moving their workloads from clunky on-premise data centers to nimble cloud platforms, orchestrated container environments, such as Kubernetes and Red Hat OpenShift, or a combination of both. The technical aspects of such a migration are typically well-known. Your IT staff does a great job managing these environments: Still, there is one more aspect of managing these environments that is often overlooked — cost.

Just launched outage and recovery alerts via Slack, right into your team's workspace and across all your Slack-enabled devices.

It’s important to be able to look at the entirety of your application architecture, not just specific aspects of it, and understand how different parts connect. Observability comes first, followed by monitoring. In this post, we’ll dive into the database part of your architecture to show how you can monitor and optimize your database performance.

When Logz.io was founded in 2015, we set out to simplify logging with the ELK Stack by delivering Elasticsearch and Kibana as a managed cloud service. But logs only tell part of the story – DevOps teams also need metric and trace data to better monitor the health and performance of their environment and quickly pinpoint the root cause of new problems. Importantly, using multiple tools to collect and analyze this data adds complexity and extra work.