Operations | Monitoring | ITSM | DevOps | Cloud

Latest News

Rails Migration A Complete Guide

A Rails migration is a tool for changing an application’s database schema. Instead of managing SQL scripts, you define database changes in a domain-specific language (DSL). The code is database-independent, so you can easily move your app to a new platform. You can roll migrations back, and manage them alongside your application source code.

Where to Find IIS Log Files

Microsoft Windows Internet Information Services (IIS) log files provide valuable information about the use and state of applications running on the web. However, it’s not always easy to find where those files are to determine important aspects of app usage like when requests for servers were made, by whom, and other user traffic concerns.

Kubernetes vs Docker: How to Choose

If you’re thinking about using containers to manage an application, there are a lot of options for technologies to use. It can be difficult to even know where to begin to make a decision. One common question is whether someone should use Docker vs Kubernetes for managing their application containers. This is a misleading question. In truth, Docker and Kubernetes aren’t competing technologies. There’s no need for them to face off.

User Story: Epic Hospital Reduces Logon Times by 80%

The below screen shots were provided by a large non-profit healthcare organization that includes 4 acute care hospitals, over 20 clinics and 5,000 Citrix users. The Healthcare IT team received reports from clinicians about slow logon times. This document describes how the Citrix engineer used Goliath Performance Monitor to pinpoint and troubleshoot the “Citrix is Slow” complaint and implement a fix action that permanently resolved the issue while reducing logon times by more than 80%.

Distributed Machine Learning With PySpark

Spark is known as a fast general-purpose cluster-computing framework for processing big data. In this post, we’re going to cover how Spark works under the hood and the things you need to know to be able to effectively perform distributing machine learning using PySpark. The post assumes basic familiarity with Python and the concepts of machine learning like regression, gradient descent, etc.