We’re excited to announce that as part of our expanded alliance with Sumo Logic, observIQ extended its support for Sumo’s platform. This allows customers to send logs and metrics to Sumo Logic, leveraging our telemetry pipeline, BindPlane. We’ve also made it possible to automatically recommend processors in our pipeline that format data specifically as Sumo Logic expects—once Sumo Logic is a destination for BindPlane.

In complex IT environments, monitoring and observability tools are indispensable. They help organizations ensure optimal performance of applications and infrastructure, providing insights and alerts to address potential issues before they impact users. Two of the leading tools in this space are Datadog and Dynatrace. This article offers a comprehensive comparison of these platforms to help you decide which is best for your needs in 2024.

This is the first of a planned series of blog posts that explain how the Cribl SRE team builds, optimizes, and operates a robust Observability suite using Cribl’s products, Cribl.Cloud operates on a single-tenant architecture, providing each customer with dedicated AWS accounts furnished with ready-to-use Cribl products. This provides our customers with strict data and workload isolation but presents some interesting and unique challenges for our Infrastructure and operations.

In recent years, hybrid work approaches have grown increasingly commonplace, and for a significant percentage of users, VPN is the go-to approach for accessing secured corporate resources and services. In fact, one article reveals that 72% of desktop and laptop users employ a VPN. As the reliance on hybrid work models and VPN connectivity continues to grow, VPN health has emerged as a critical success factor for businesses.

When it comes to monitoring, diagnosing, and optimizing the performance of complex systems today, you can’t really go wrong with tracing tools. And while OpenTelemetry has become the go-to choice for instrumenting apps and collecting traces, there are several other options in the backend that can effectively store, manage, and analyze traces sent by OpenTelemetry. Two of these open-source tools are Jaeger and Grafana Tempo. In this article, we’ll compare and contrast the two.

While traditional IT monitoring holds back Australian small and medium-sized enterprises (SMEs) in digital transformation, these organizations do realize that in the realm of IT operations, observability represents a significant advancement over traditional monitoring approaches. Unlike conventional methods that primarily focus on metrics like uptime and error rates, IT observability provides a comprehensive view of system behavior by integrating logs, metrics, traces, and events.

The previous post laid out the basic idea of instrumentation and how OpenTelemetry’s auto-instrumentation can get teams started. However, you can’t rely only on auto-instrumentation. This post will discuss the limitations in more detail and how a CoPE can help teams overcome them.

Anthropic is an AI research and development company focused on building reliable and safe artificial intelligence systems. Their flagship product is Claude, an advanced language model and conversational AI assistant known for its strong capabilities in natural language processing, reasoning, and task completion. Anthropic places a particular emphasis on AI safety and ethics, and its models and APIs are used by organizations across various industries to build powerful, safe, and performant AI applications.

Elastic Observability 8.15 announces several key capabilities: New and enhanced native OpenTelemetry capabilities: Elastic AI Assistant enhancements: Large language model (LLM) observability for Azure OpenAI: Elastic Observability now provides deep visibility on the usage of the Azure OpenAI Service. The integration includes an out-of-the-box dashboard that summarizes the most relevant aspects of the service usage, including request and error rates, token usage, and chat completion latency.

The cloud environment has generated an unprecedented volume of data, making it increasingly difficult for enterprises to manage. With multiple SaaS and cloud-based applications in play, differentiating which data needs processing for analysis versus storage for regulatory compliance is a significant challenge. The growing number of data sources only complicates this further. So, getting clarity and control over this chaos is the goal, without having to overhaul your entire system.