Operations | Monitoring | ITSM | DevOps | Cloud

In previous posts we covered why it’s important to monitor APIs and how to monitor and validate data from APIs. In this post we’ll focus on a simple but key feature that helps Splunk Synthetic Monitoring users create robust checks for availability, response time, and multi-step processes: Request Headers

In a recent episode of the Cloud Happens podcast, Archana Venkatraman, Associate Research Director in Cloud Data Management at IDC Europe talks about how the cloud isn’t a destination. It’s a continuum; a journey. In this blog, we explore that idea a bit more and dive into what really encapsulates a cloud experience. How can modern enterprises benefit from their cloud journey to solve the most gnarly data challenges to unlock innovation, enhance security, and drive resilience.

Monitoring is as important as application development to keep an application running healthy for the best user experience. For this reason, a strong monitoring strategy is essential for your company's success, ensuring that metrics such as constant performance, high availability, and accessibility are never a concern. Many businesses neglect the importance of frontend monitoring for their applications.

There are standalone distributed tracing tools like Jaeger, and there are APM tools that do not provide distributed tracing capabilities. In this article, we will see how distributed tracing complements an APM tool for a holistic performance monitoring experience. Both APM and distributed tracing are critical tools to understand the performance of your applications. And if your application is facing performance issues impacting customer experience, you need to understand what’s causing it fast.

A multicloud strategy is a necessity for modern businesses, as the recent AWS outages made clear, but managing this infrastructure remains a huge challenge. Infrastructure management teams have long struggled to juggle diverse technology solutions, policies and services to get access to a point-in-time view of their resources. The result is either waste through overprovisioning or huge overheads for nitpicking manual management and repetitive tasks.

There is a lot of buzz in the world of machine learning (ML) and as a layperson it can be hard to keep up with it all. Therefore, we decided to write down some of our thoughts and musings on how we are approaching ML at Netdata. We’ll touch on the current state of applied ML in industry in general, and zoom in on ML in the monitoring industry.

Each Avantra version packs powerful features and enhancements that make it even simpler to work with and integrate - leading to better system quality, resilience and compliance. Avantra 21.11.4 lives up to this expectation by offering the new remote Java monitoring support feature.

AWS Lambda is AWS’s solution for highly portable, serverless computing. With Lambda functions, you can deploy and run business logic code without managing the underlying servers. Today, AWS announced that Lambda customers can now provision up to 10 GB of ephemeral storage for each of their functions, making them well-suited for new, data-intensive workloads—including machine learning inference, large media file processing, financial analysis, and more.