Cyber crimes are expected to cost the world roughly $10.5 trillion per year by 2025, according to Cybersecurity Ventures. And these attacks don’t just cost money. Businesses impacted by these kinds of crimes can expect to experience not only financial losses but also loss of productivity, damage to their reputation, potential legal liabilities and more.

DevOps teams are used to working with data that is spread out across lots of different systems and environments. In organizations that have achieved tight collaboration with security teams to transition to DevSecOps, this is even more true! Log management is part of how all these teams keep track of information and make vital business decisions. It’s important to take a moment to understand what is meant by log management.

Your Apache HTTP server access and error logs contain a wealth of actionable insights about potential server configuration and web application issues. The problem is that this information is hidden within millions of log messages, so you need analytics to efficiently extract these insights so you can respond to problems before they impact your users. Apache log analysis revolves around two activities: monitoring and troubleshooting.

Want to build websites and apps in a way that’s both easier and cheaper? Well, it’s possible even for major organizations and international companies. In this article, let’s take a look at how serverless architecture and computing is changing the game for software developers. We’ll start at the very beginning and walk through how serverless works, how we got this far, and the pros & cons of this approach.

Upon its release in November 2022, ChatGPT stunned Silicon Valley and the world. OpenAI, a small company based in San Francisco, introduced a chatbot that mimics complex emotions, writes code and answers complex questions. Technology considered a decade away was now at everyone’s fingertips and quickly became the fastest-growing app in history. Just four months later, OpenAI launched a significant update, ChatGPT-4, and the results of this new technology are fascinating.

ChatGPT is so hot right now, it broke the internet. As an avid user of ChatGPT and a developer of ChatGPT applications, I am incredibly excited by the possibilities of this technology. What I see happening is that there will be exponential growth of ChatGPT-based solutions, and people are going to need to monitor those solutions.

The shortcoming of traditional SIEM implementations can be traced back to big data analytics challenges. Fast analysis requires centralizing huge amounts of security event data in one place. As a result, many strained SIEM deployments can feel heavy, require hours of configuration, and return slow queries. Logz.io Cloud SIEM was designed as a scalable, low-maintenance, and reliable alternative. As a result, getting started isn’t particularly hard.

ChaosSearch was built for live analytics at scale on cloud storage. Our architecture was designed for high volume ingestion of streams & analytics at scale via ElasticSearch & Trino API via a stateless fabric that can scale to meet the customers’ scale & latency requirements. Because we don’t store any data, under the hood, ChaosSearch is basically a set of containers that are deployed in cloud compute instances in a dedicated VPC to each customer managed by ChaosSearch.

Datadog is a powerful observability platform. However, unlocking it’s full potential while managing costs necessitates more than just utilizing its platform, no matter how powerful it may be. It requires a strategic approach to data management. Enter telemetry pipelines, a key to elevating your Datadog experience. Telemetry pipelines offer a toolkit to achieve the essential steps for maximizing the value of your observability investment. The Mezmo Telemetry Pipeline is a great example of such.

How machine learning in AppDynamics Cloud accelerates log analysis and reduces mean time to detect. Site recovery engineers (SREs) need to investigate unknown problems reported in production. The common approach is to search and filter log files to find the root cause, and we all know how painful it is to sift through log contents. It’s like finding a needle in a haystack. A machine learning approach is essential to assist SREs to quickly identify the root cause.