Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

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.