Today we are excited to announce scheduled searches – a new feature on Dashbird that allows you to track any log event across your stack, turn it into time-series metric and also configure alert notifications based on it. This has been one of the most requested features across our users and we are thrilled to make it available for all users starting today.

Most sentences that include both on premises and cloud usually put the word “or” between them, or perhaps “vs.” But most enterprises operate in the world of “and.” In other words, they have workloads on premises and in the cloud—and that little three-letter word makes a world of difference.

Have you ever had to set up and/or maintain a local development environment using Docker that mirrors your production environment as much as possible? Setting-up and supporting such a docker-based local environment is no easy feat! Common problems include performance issues, broken dependencies, broken relationships between your services and uncontrolled updates of your docker images, among many other issues. So, the question is, what can we do about that?

Have you ever had to set up and/or maintain a local development environment using Docker that mirrors your production environment as much as possible? Setting-up and supporting such a docker-based local environment is no easy feat! Common problems include: performance issues, broken dependencies, broken relationships between your services and uncontrolled updates of your docker images, among many other issues. So, the question is, what can we do about that?

FinOps, or the practice of managing a company’s cloud computing costs, and DevOps, the process of efficiently building applications in the cloud, should ideally go hand-in-hand. Engineers with a streamlined development process and a cost-conscious mindset can produce functional products with great margins and a high potential for scalability. However, incorporating FinOps into the DevOps lifecycle, and vice versa, isn’t always easy.

With Eco’s automated reservation management, maximizing savings on your cloud bill and increasing your team’s bandwidth is easy. The new dashboard now includes responsive modular components and an improved commitment filter, so tracking savings and monitoring your environment is even easier.

The best thing about moving into a new home is getting to call it your own. But the second-best thing is coming up with a funny wifi name you can tell your friends about. As people say, “home is where the wifi connects automatically!” And you certainly shouldn't leave its name up to chance. While setting up your internet is one of the first things you do after moving, simply connecting to a random scramble of characters does not feel very personal, does it?

The thermostat for the first floor of my house is located right behind me in my home office. I probably walk by it a dozen times a day, but I only look at it if I am feeling cold or warm to check to see if the temperature is set to the correct value. In Massachusetts, hot and cold are seasonal but can vary a lot day to day so checking occurs more often than you would think.

Amazon SageMaker makes it easy to prepare data for machine learning (ML) and then train, deploy, and modify ML models. SageMaker is a fully managed service that automates much of the ML lifecycle. So, if you want a single partner to help you through all stages of your Artificial Intelligence (AI) lifecycle, SageMaker might be the answer. Perhaps more important for this post is the promise that Amazon SageMaker can reduce your machine learning model costs. But does SageMaker pricing reflect this?

In 2020, remote work exploded with the start of COVID-19 as businesses scrambled to stay productive and competitive amidst health concerns and quarantines. During the pandemic, close to 70% of workers were working from home. Currently, 4.7 million people work remotely at least half the time in the US, and 62% of workers claim to work remotely at least occasionally.