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Trace spans are captured in the runtime after decrypting the request. This means that any sensitive data is available in plain text. This is also the case for logging; however, logging requires an explicit log statement to be coded by the engineer. Additionally, engineers can add arbitrary information to trace spans, which could expose sensitive information. Collecting sensitive information in trace spans or logging events could expose an organization to a number of risks.

There is no escaping the discussion about how machine learning (ML) and AI systems will revolutionize how people and industries work. Most of this discussion needs to be revised, as companies are still evaluating how AI systems (typically Large Language Model (LLM) systems like OpenAI ChatGPT, Google Gemini, Anthropic Claude and others) enhance worker productivity and deliver business benefits. Cybersecurity is one sector where extensive use of AI-enhanced solutions is common.

Developed in the mid-1990s, Ruby is a dynamic, open-source programming language. The tool has grown in popularity from its initial release, having been used in modern systems covering a variety of corporate and academic use cases. Ruby gained further traction after the release of Ruby on Rails, a powerful web application framework written in pure Ruby.

Logstash serves as a real-time event processing engine within the OpenSearch ecosystem, which comprises OpenSearch, Beats, and OpenSearch Dashboards. Logstash acts as a centralized pipeline that collects data from numerous sources before forwarding it to OpenSearch.

This blog introduces the RED method—an approach that simplifies microservices monitoring by honing in on requests, errors, and latency.

While New Relic has long been a popular choice for Application Performance Monitoring (APM), the tech landscape has brought forth several compelling alternatives. This guide provides an in-depth look at the top New Relic competitors and alternatives including open-source, comparing their features, strengths, and use cases to help you make an informed decision for your organization's needs.

Power Usage Effectiveness (PUE) is a metric for measuring the overall energy efficiency of a data center facility. It is calculated by dividing the total amount of energy consumed by the facility by the energy used specifically by the IT equipment. This metric is useful to data center managers because optimizing for it can lead to better energy efficiency, lower operational costs, reduced environmental impact, and support regulatory compliance and certification requirements.

Keeping smooth and safe operations now relies entirely on observability. But as there's more and more data to keep track of, the costs are going up. This makes it hard for your companies to balance how well things are running and their budgets. OpenTelemetry can help by making a standard way to collect and process all the data. We're going to share how OpenTelemetry can save you money on observability and why having too much data can be costly.