Splunk’s extensible and scalable data platform has been instrumental in helping ITOps teams fully understand their tech environments and tackle any IT use case with data streaming, dashboarding, federated search, AI/ML, and more. But, with the explosion of telemetry and the growing complexity of digital systems, ITOps practitioners who rely solely on a logging solution are missing out on critical insights from their digital systems.

Site reliability engineers (SREs) and security analysts — despite having very different roles — share a lot of the same goals. They both employ proactive monitoring and incident response strategies to identify and address potential issues before they become service impacting. They also both prioritize organizational stability and resilience, aiming to minimize downtime and disruptions.

On October 1, 2022, the UK Telecommunications Security Act (TSA) went into effect, imposing new security requirements for public telecom companies. The purpose of the act is noble, as it wants to ensure the reliability and resilience of the UK telecommunications network that underpins virtually every aspect of the economy and modern society.

Stackify Retrace is an application performance management (APM) and log management platform designed to assist developers and DevOps teams in tracking, troubleshooting, and enhancing the performance of their applications and infrastructure. Stackify Retrace effectively combines APM with log management, enabling users to view detailed transaction traces for applications directly from the log statement to provide greater context and visibility for more effective analysis.

I recently attended SRECon in San Francisco on March 18 - 20, a show dedicated to a gathering of engineers who care deeply about site reliability, systems engineering, and working with complex distributed systems at scale. While there were a lot of talks, I’ll focus on a few areas that gave me the most insight into how having the right data impacts an SREs and an organization’s success.

Recently, Elastic announced the AI Assistant for Observability is now generally available for all Elastic users. The AI Assistant enables a new tool for Elastic Observability providing large language model (LLM) connected chat and contextual insights to explain errors and suggest remediation.

In software development, two primary models of software exist: open source and closed source. Both types have their benefits and drawbacks, and understanding the differences between them can help you make informed decisions when choosing software for your projects. To simplify the concepts of open source and closed source software, let’s use the analogy of community cookbooks — open source — and a secret family recipe: the closed source.

Cribl Search helps find and access data regardless of the format it’s in or where it lives. Search provides a federated solution that reaches into existing object stores and explores data without moving it or having to index it first. This same interface can also connect to APIs, databases, or existing tooling, and can even join results from all these disparate datasets and display them in comprehensive dashboards.

Adopting OpenTelemetry native standards for instrumenting and observing applications If you develop applications, you may have heard about OpenTelemetry. At Elastic®, we are enthusiastic about OpenTelemetry as the future of standardized application instrumentation and observability.

Every software-driven business strives for optimum performance and user experience. Observability—which allows engineering and IT Ops teams to understand the internal state of their cloud applications and infrastructure based on available telemetry data —has emerged as a crucial practice to help engage this process. For years, application performance monitoring (APM) was the de facto practice and tooling that organizations have used to keep tabs on their critical systems.