Elasticsearch and Splunk are two leading solutions renowned for their capabilities in processing, analyzing, and visualizing large datasets in real-time. Both platforms have carved out significant roles in the fields of data analytics and log management, each offering unique features tailored to different needs. This article aims to provide a comprehensive comparison of Elasticsearch and Splunk, highlighting their strengths and weaknesses, and introducing Uptrace as a compelling alternative.

With the increasing complexity of modern applications, log management solutions have become synonymous with troubleshooting, monitoring, and ensuring application reliability. Moreover, choosing the right tools can significantly impact your application’s performance, efficiency, and overall operational costs. Two powerful tools that often come up in these discussions are Grafana Loki and the ELK Stack (consisting of Elasticsearch, Logstash, and Kibana).

At Mezmo, we consider Understand, Optimize, and Respond, the three tenets that help control telemetry data and maximize the value derived from it. We have previously discussed data Understanding and Optimization in depth. This blog discusses the need for responsive pipelines and what it takes to design them.

SecOps teams at midsize companies face a unique set of challenges when it comes to managing organizational cybersecurity. Midsize companies (those with 100-999 employees and $50 million-$1 billion in annual revenue, according to Gartner) possess significant financial resources and valuable data that may be targeted by digital adversaries.

Kubernetes has just reached its 10th anniversary, signifying the maturity of the containers movement. Now it’s time to explore the next frontier in cloud-native evolution: WebAssembly, a.k.a. WASM or Wasm. Moving beyond containers and Kubernetes, WASM bears the promise to revolutionize the cloud landscape with unparalleled performance, portability, and security.

Today’s distributed IT infrastructure consists of many services, systems, and applications, each generating logs in different formats. These logs contain layers of important information used for data analytics, security monitoring, and application debugging. However, extracting valuable insights from raw logs is complex, requiring teams to first transform the logs into a well-known format for easier search and analysis.

The SIEM market attracts attention for a variety of reasons. First, it is dominated by a number of large players but there are a range of smaller companies vying for market share. It is also a market generally accessible to new entrants. There’s always a new company pitching a different spin on SIEM, whether it’s a new architectural model in the cloud, faster analytics from running on a third-party data warehouse, or leaning into new, undefined terms like a security data fabric.

Human attention spans are seemingly shorter than ever, and your mobile application users are, unfortunately, no exception. Over 70% of users abandon an app if it’s taking too long, with half of these users waiting no more than three seconds. Even minor delays or errors can lead to significant user drop-off, negatively impacting your app’s success and user satisfaction.

Integrations are the bread and butter of building vendor-agnostic software here at Cribl. The more connections we provide, the more choice and control customers have over their unique data strategy. Securing these integrations has challenges, but a new class of integrations is creating new challenges and testing existing playbooks: large language models. In this blog, we are going to explore why these integrations matter, investigate an example integration, and build a strategy to secure it.

Telemetry data collection and analysis are important for businesses. We're diving right in to explain the ins and outs of the OpenTelemetry Collector, including its core components, distribution selection, and customization tips for optimal data collection and integration. Whether you're new to OpenTelemetry or expanding your capabilities, this will help you effectively use the OpenTelemetry Collector in your observability strategy.