Logz.io is excited to announce Service Map, a new way to visualize the data flow, dependencies, and critical performance metrics throughout your microservices architecture, which makes it easy to gather critical troubleshooting context as you investigate production issues.

One of the most important events you should be monitoring on your network is failed and successful logon events. What comes to most people’s minds when they think of authentication auditing is OS level login events, but you should be logging all authentication events regardless of application or platform. Not only should we monitor these events across our network, but we should also normalize this data so that we can correlate events between these platforms.

To monitor and troubleshoot the performance of microservice-based applications, Jaeger and Zipkin are examples of the most commonly used open-source distributed tracing systems. They both supply users with insight into the flow of requests through various components of a system, which can be utilized to find latency bottlenecks, errors, and performance problems in the system.

Let’s be honest, working with Kubernetes (K8s) has never been the easiest tech to work with. As a seasoned Kubernetes professional, I find myself constantly looking for ways to set up collecting data from my clusters, only to find out that there is a new, more complicated way to get the data I’m looking for.

Our previous post was all about dipping your toes into the wonderful world of API interaction. By leveraging Cribl’s API you can automate many parts of your event pipeline management and tasks. So we got that goin’ for us. Which is nice. One of the common use cases for the API I hear about is kicking off data collection automatically. Use cases include: Cribl gives you the tools to collect data when you want, from where you want, and to where you want.

It’s time to stop firefighting. With Sumo Logic’s AWS Observability, companies like Snoop have been able to simplify data collection, achieve unified visibility across AWS accounts and regions and leverage machine learning to troubleshoot — fast. This re:Invent, we’re excited to showcase how our capabilities for AWS have evolved.

The open-source Kubernetes platform has become the de facto standard for deploying, managing, and scaling containerized services and workloads. In fact, 83% of DevOps teams are using Kubernetes to deploy containerized applications in production, taking advantage of its workload orchestration and automation capabilities to optimize the software development process and reduce web server provisioning costs.

A Global 1000 E-commerce company struggled with the rapid growth in telemetry data that their security team analyzes with Splunk, Grafana, and other Observability tools in the cloud. Specifically, the increase in VPC Flow log and Firewall log volumes caused a spike in Splunk costs on certain data sets and triggered daily indexing limit overage fees. As this deluge of data began piling up in block storage within their Splunk index, the team saw corresponding spikes in storage costs.

A large, global Data Management and AI software company with over 5,000 customers across more than 100 countries had seen unprecedented growth (more than 30% year over year) in telemetry data from their multi-cloud infrastructure being sent to the Elasticsearch Observability and SIEM Platform. The growth of this data contributed to a multi-million dollar price tag for Elasticsearch.

In the realm of modern software development, achieving true observability is paramount for understanding application behavior and performance. This demonstration focuses on a.NET application that harnesses the capabilities of OpenTelemetry to seamlessly integrate logging and tracing functionalities. OpenTelemetry, a key player in the Cloud Native Computing Foundation, provides a unified framework for comprehensive observability.