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A Closer Look at Kubernetes: Its Origins and Why It Matters

Since Google open sourced Kubernetes in 2014, it has become one of the most popular open source projects. Adopted by all major cloud providers, Kubernetes has undoubtedly become the de facto container orchestrator. But what is Kubernetes and container orchestration really about? Without the technical background and knowledge about the technology developments that preceded it, it can be challenging to wrap your head around it.

ChatOps-The future of collaboration

ChatOps is the implementation of chatbots to unify communication and collaboration. Through ChatOps every single member of a team will be aware of what the other members are working on. It is the logical next step in the evolution of communication among teams after email and IM. Projects of today are developed at a global scale with millions of people as potential users, this means that teams are larger and often work in shifts or even remotely.

Introducing the lumigo-cli

Here at Lumigo, we are big fans of serverless. And a big part of working with AWS Lambda involves using many other AWS services. For example, services such as SNS and SQS are often used to chain Lambda functions together. They are essential ingredients of an event-driven architecture, where systems are loosely coupled through events. However, they also pose a challenge to how we test our systems and how to get fast feedback on what’s happening in the system.

Don't Treat Your Business Metrics Like Other Metrics

Many companies today try to feed business metrics into APM or IT monitoring systems. Splunk, Datadog and others track your business in real time, based on log or application data – something that would seem to make sense. In practice, however, it fails to produce accurate and effective monitoring or reduce time to detection of revenue-impactful issues. Why? Because monitoring machines and monitoring business KPIs are completely different tasks.

IBM Log Analysis with LogDNA

IBM Cloud Log Analysis with LogDNA enables you to quickly find the source of issues and gain deeper insight into application and cloud environment data. IBM Cloud logging begins with log aggregation from application and services within IBM Cloud. IBM partners with LogDNA to bring collection, log tailing and blazing fast log search. LogDNA supports integrations to many cloud-native runtimes and environments.

The Top 3 Use Cases for Machine Learning in Analytics and Monitoring

It’s no secret that machine learning (ML) has experienced tremendous growth and adoption over the last few years. And why not? This exciting technology has enabled us to utilize the power of machines for a wide variety of applications and industries. From image processing to predicting to medical diagnosis, ML has begun to reshape the way we live.

Growth Forecasting Use Cases for Anodot's Autonomous Forecast Solution

Every successful company plans for sustainability and growth. Forecasting the growth path helps companies set their short- and long-term business objectives and make important decisions to help them reach their goals. Short-term forecasts are important in quarterly and annual budget planning and for ensuring that daily business operations help achieve long-term goals.

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The 8 CI/CD tools you need to know about in 2019

In 2019, one part of a successful development team is having a solid CI/CD pipeline. Now, every pipeline will have a unique set of outcomes and needs-which means that you'll need a strong set of tools to help you accomplish your goals. This blog post will help identify some of the tools out there that can help you make your pipeline great. These tools range from the familiar Jenkins and its newer predecessor Jenkins X to security tools like Twistlock. First, we'll cover what it means to have a CI/CD pipeline. Then we'll explore tools that can help you create and run a pipeline, add better security, and even help you deploy.

Grafana Labs at 5: How We Got Here and Where We're Going

In the beginning, there was a developer using Graphite, and he found its user interface lacking. Then he discovered the Kibana project, liked its UI, and forked it. Grafana was born in 2013. “I started Grafana to do something similar as Kibana, but focused on time series metrics. My goal was to make time series data accessible for a wider audience, to make it easier to build dashboards, to make graphs and dashboards more interactive,” says Torkel Ödegaard.