Linux is one of the most popular and widely used open-source operating systems in the world today. It provides the key advantage of stability, compatibility, security and customization. Since Linux servers are the backbone of an organization’s IT infrastructure, a sudden change in the CPU usage or memory can adversely affect the performance of applications.
The emergence of GitOps has been an interesting and important recent development in the Infrastructure as Code (IaC) space.
Companies are leveraging the power of Kubernetes to accelerate the delivery of resilient and scalable applications to meet the pace of business. These applications are highly dynamic, making it operationally challenging to securely connect to databases or other resources protected behind firewalls.
We continue with our series of articles on containers. First, we started with creating our own images with Docker Build and saw how to run them with Docker run. But today we will learn what Docker Compose is and start our journey into the world of container orchestration. Up to now we have managed containers manually and separately, which for some specific test may be valid and functional. But when the number of containers to manage starts growing, this method becomes infeasible.
Elastic 7.6 introduced the inference processor for performing inference on documents as they are ingested through an ingest pipeline. Ingest pipelines are incredibly powerful and flexible but they are designed to work at ingest. So what happens if your data is already ingested? Introducing the new Elasticsearch inference pipeline aggregation, which lets you apply new inference models on data that's already been indexed.
Chances are you already spent a big part of your day looking for a document, an email, or an answer that lies deep within a Google Slides presentation. Thankfully, you landed in the right place. With Workplace Search, finding the right information across all your cloud and on-premises data platforms is now easier than ever, and it’s a few clicks closer than you expect.
Many organizations are transitioning toward a DevOps operational model, where software developers are responsible for operating the applications they develop, instead of a centralized IT operations group. In this “CTO Perspective” interview we talk to BigPanda’s CTO Elik Eizenberg about the challenges in that transition, and what it takes to make it easier. Lean back and watch the interview, or if you prefer reading, take a few minutes to read the transcript.
Welcome to the final installment of our Complete AWS Lambda Handbook series! Given Lambda is often the central point for many serverless applications, we wanted to make sure we didn’t skip or breeze past any part. In this episode, we’re looking at some limitations and difficulties using AWS Lambda and how to overcome them, and the importance of monitoring for performance and failure remediation.
I have been following distributed tracing technologies — Zipkin, OpenTracing, Jaeger, and others — for several years, without deeply trialing with any of them. Just prior to the holidays, we were having a number of those “why is this slow?” questions about an express application, written in typescript, providing an API endpoint.
