Establishing a proactive security posture involves a data-driven approach to threat detection, investigation, and response. In the past, this was challenging because there wasn’t a centralized way to collect and analyze security data across sources, but with Amazon Security Lake it is much simpler.

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.

Data volumes are soaring. Environments are increasingly intricate. The risk of applications and systems encountering breakdowns is sky-high, and the mean time to recovery (MTTR) for production incidents is moving in the wrong direction. Disruptions not only jeopardize critical infrastructure but also have a direct impact on the bottom line of organizations. Swift recovery of affected services becomes paramount, as it directly correlates with business continuity and resilience.