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.

As data keeps growing at incredible rates, it’s becoming increasingly difficult to store and monitor at a reasonable cost leaving you to cherry-pick which data to store. As developers are accustomed to integrating metrics within their logs and spans, this can result in poor monitoring & analysis, alert fatigue, and longer MTTR. Teams are left having to dig out the most relevant data, which results in missed trends and analysis.

In today’s data-driven landscape, every organization knows the immense value their data holds, but with the explosion of data from diverse sources, traditional data storage and management solutions are proving inadequate. Organizations are urgently seeking new ways to handle their data effectively.

As the automation of processes and deployment becomes more prevalent in the manufacturing industry, the need for IT services grows further. The use of complex systems and technologies, such as AI and robotics has become the new normal for manufacturing organizations.

Observability goes beyond monitoring; it's about truly understanding your system. To achieve this comprehensive view, practitioners need a unified observability solution that natively combines insights from metrics, logs, traces, and crucially, continuous profiling. While metrics, logs, and traces offer valuable insights, they can't answer the all-important "why." Continuous profiling signals act as a magnifying glass, providing granular code visibility into the system's hidden complexities.