Being elastic in nature, many of us think migrating to cloud will be a cost-effective solution to host infrastructure. But this is partly true, however, when your hosted infrastructure becomes fat the cost of the cloud increases rapidly. Moreover, managing cloud infra costs becomes difficult because of the growing infrastructure/teams. To curb the increasing costs, here are a few best practices which can be adhered to manage infrastructure efficiently.

If you liked Sematext Docker Agent you’ll love our new agent for Docker monitoring that provides you with even more insight into your Docker, Kubernetes, and Swarm clusters. Because of its power, small footprint, and ease of installation the old Sematext Docker Agent enjoyed high adoption by the Docker DevOps community.

Large organizations that wish to scale at an aggressive pace need IT departments that can be both nimble and agile. With DevOps and site reliability engineering (SRE) methodologies, IT teams can improve the agility, availability and performance of applications and services in their infrastructure. For those who are new to both concepts, here is a primer on how DevOps and SRE can work together to evolve IT operations.

Developers often believe that demonstrating the need for an IT-based solution should be very easy. They should be able to point to the business problem that needs a solution, briefly explain what technology should be selected, and the funds, staff, and computer resources will be provided by the organization. Unfortunately, this is seldom the actual process that is followed.

Everyone’s infrastructure is growing – today mostly in the container space. As we learned in Part 1 of this series – Docker Container Monitoring and Management Challenges, monitoring for containers is different from traditional server monitoring. In Part 2 we had a glance at key container metrics and in Part 3 we compared several open source tools for container monitoring.

Serverless computing is all the rage at the moment, and why wouldn’t it be? The idea of deploying code without having to worry about anything like servers, or that pesky infrastructure everyone complains about seems pretty appealing. If you’ve ever used AWS lamdba or one of its related cousins, you’ll be able to see the freedom that triggering functions on events brings you.

For about a year, since we incorporated CloudHedge.io and during the 18 months of product building before that, we spoke with hundreds of prospects (both partners and clients included). Our team worked directly with clients and with partners, enabling them to try the product and later on-board them. While Partners are big global SI’s, customers range between SME to Fortune 100 companies.

Gigantic amounts of data are being generated at high speeds by a variety of sources such as mobile devices, social media, machine logs, and multiple sensors surrounding us. All around the world, we produce vast amount of data and the volume of generated data is growing exponentially at a unprecedented rate. The pace of data generation is even being accelerated by the growth of new technologies and paradigms such as Internet of Things (IoT).

The Kubernetes ecosystem contains a number of logging and monitoring solutions. These tools address monitoring and logging at different layers in the Kubernetes Engine stack. This document describes some of these tools, what layer of the stack they address, as well as best practices for implementation including an example from the field, a quick start, and a demo project.

Stackdriver Monitoring contains a wealth of information about cloud resource usage, both for Google Cloud Platform (GCP) and and other sources. This post will explain how to use the Stackdriver Monitoring API to read, downsample, and export data from Stackdriver to BigQuery. Pub/Sub metrics will be used to demonstrate this.