One of the biggest benefits of the cloud is that it gives engineering teams the freedom to deploy and iterate applications quickly. Unlike traditional IT environments where engineers require a series of approvals before embarking on projects, in the cloud, engineers can choose from several managed services and deploy them at the click of a button. This means your team can innovate faster and respond quickly to market demands.

Whether you've recently migrated to the cloud, or you've been operating there for awhile now, you've likely realized how challenging it can be to understand exactly where your cloud spend goes and what drives your costs. Using a cloud service provider can offer many benefits, like the ability to easily scale up and down based on need, but unless you're using the right services and tools, your costs can quickly spiral out of control.

The move to cloud computing has been a no-brainer for many enterprise companies. But cloud computing is an expense that, unlike many other operating costs, is largely variable. Many companies — including the fastest-growing startups, largest enterprises, and leading government agencies — choose AWS to help them streamline fragmented processes, reduce costs, become more agile, and innovate faster.

Containers and serverless computing are two of the most popular methods for deploying applications. Both methods have their advantages and disadvantages. To choose the one that’s right for your business, you need to understand the pros and cons of managing your own containers versus using serverless services.

It’s been a great month for exciting insights about the role that engineering must play in driving profitable innovation.

Big data analytics help organizations use data to explore both new and improvement opportunities. Whichever cloud data platform you choose, there are two data storage technologies you will want to understand. Data warehouses and data lakes are the two dominant data solutions commonly used for defining how an organization stores, queries, analyzes, and reports on big data. This post will define what a data warehouse and data lake are, how they work, and their differences.

In this enterprise cost containment series, we’ve tackled a range of topics from cloud to professional services and more. Now, I want to dive into the topic you may have expected us to cover from the start: monitoring. After all, at SolarWinds, we create monitoring software. The goal of this post is not to present or sell software, though. It’s our intention to help you have conversations with management and stakeholders—no matter the monitoring you use.

AWS tags are a bit like flossing your teeth every day — or getting eight hours of sleep a night. Everyone agrees they’re good habits that will make life easier down the road. But sometimes life gets in the way, and those habits fall a little short. Most teams set out with the intention to tag their infrastructure, but in our experience, it’s rare that companies have a perfect, thorough AWS tagging strategy.

The experienced IT professional is no stranger to the term “automation.” However, many don’t fully grasp the potential this word presents to IT processes, including the fast-growing realm of intelligent automation. But before you start looking to automation to help rein in costs, it’s important to develop an automation strategy that best fits the needs of your organization. This article explores the three key areas in which to start when building your automation framework.