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.

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.

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.

Faced with an important business decision? Do you have the data you need to make it? Odds are, you probably don’t. Or, if the data is captured somewhere, can you count on it being in one place and easily accessible? This is a common issue, easily solved by proper log management. This practice is vital for data-driven businesses, helping you maintain security, troubleshoot operations more quickly and enhance user experience.

It is commonplace for organizations to restrict their IT systems from having direct or unsolicited access to external networks or the Internet, with network proxies serving as gatekeepers between an organization’s internal infrastructure and any external network. Network proxies can provide security and infrastructure admins the ability to specify specific points of data egress from their internal networks, often referred to as an egress controller.

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.

If you’re in need of new SIEM tooling, it can be more complicated than ever to separate what’s real and what’s spin. Yes, Logz.io is a SIEM vendor. But we have people in our organization with years of cybersecurity experience, and they wanted to share thoughts on how best to address the current market. Our own Matt Hines and Eric Thomas recently hosted a webinar running through what to look out for titled: Keep it SIEM-ple: Debunking Vendor Nonsense. Watch the replay below.

The blog will take you through best practices to observe Kafka-based solutions implemented on Confluent Cloud with Elastic Observability. (To monitor Kafka brokers that are not in Confluent Cloud, I recommend checking out this blog.) We will instrument Kafka applications with Elastic APM, use the Confluent Cloud metrics endpoint to get data about brokers, and pull it all together with a unified Kafka and Confluent Cloud monitoring dashboard in Elastic Observability.

Platform engineering is the practice of automating infrastructure operations and enabling self-service infrastructure capabilities within collaborative Dev, Ops and QA teams. It involves designing and building platforms, technologies and workflows that enable self-service capabilities to automatically manage, provision and operate complex modern software architecture environments.

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.