Operations | Monitoring | ITSM | DevOps | Cloud

A fundamental requirement for building a profitable product is finding ways to reduce costs and improve efficiency. When you’re operating in the cloud, it’s almost impossible to gain this insight without data. This is where data-driven software development comes in. Data-driven development is a software engineering approach that relies on data to guide the development process.

According to Amazon, On-Demand Instances are best reserved for short-term processes with uneven workloads that cannot be interrupted. This might partly explain why such EC2 instances are exceedingly popular with AWS users. On the flip side, however, it turns out that On-Demand Instances are also getting dumped for the sake of cloud cost optimization. Our recent Spot Instances guide, for instance, advises AWS users to consider making a switch and, consequently, enjoy up to 90% cost savings.

EC2 instances come with a spider web of charges — when you take a look at your bill, you’ll see there are hundreds of unique line items connected to your usage. In addition to the instance, you’re also billed for storage, data transfer, and networking associated with that instance. It’s important to fully understand those charges so you can find ways to optimize your costs.

Over the past decade, businesses have flocked to the public cloud, with the promise of faster innovation, improved scalability — and of course, cost savings. Yet for many companies, the reality of cloud migration has been a different story. CFOs are blindsided by high costs and uncontrolled spending. Meanwhile, engineering teams struggle to justify and explain their spend — often frustrated that finance doesn’t understand, even when spend is growing healthily along with the business.

FP&A is a strategic part of the finance organization and has the potential to drive important business outcomes. When done right, it can have a major positive impact on the future of the business. When done poorly, it can slow a company down. The role of FP&A has evolved. Today it isn’t just about taking inputs and crunching numbers — it’s about being a strategic advisor to the organization.

Serverless services save time. When you switch to serverless architecture, you offload redundant cloud management activities to your cloud provider and gain more time to focus on the most important parts of your business — development and innovation. Even better, you only pay for what you use, so you don’t have to worry about committing to reserved instances or committing to a savings plan. But there’s a catch.

For any company working in the cloud or using managed cloud services to create products, cloud costs represent a significant expense. If you lead an engineering team, you may have to defend your bill to your CEO or CFO. But here’s the challenge: Cloud costs can be difficult to communicate because engineers and executives often think about cost from different perspectives. As an engineer, you may think of costs in terms of AWS services, such as EC2 instances, storage, etc.

To explain how cost anomaly detection works in AWS, let’s first look at an analogy. Imagine you strategically propagate, cultivate, harvest, and replenish trees for a thriving forest products company. In keeping with your eco-friendly policy, you only harvest the trees with straight and tall trunks. You leave irregularly shaped trees alone. Your company doesn’t harvest trees you won't use.

Managing technology isn't your only responsibility as a CTO. You could be the CIO, Digital Enabler, Data Officer, and Engineering Leader all rolled up into one, whether you work at a startup, scaleup, or enterprise. Your role as a C-suite executive for engineering requires you to research and implement the newest, most efficient technologies. As CTO, you’re responsible for: The list goes on and on. That's why you, of all people, need the right tools to manage your vital role in the digital world.

There are many tangible benefits to using containers for your computing needs. Containers help break large applications into smaller packages that are more agile, scalable on-demand, resilient, cost-effective, and less resource-hungry than monolithic apps or workloads running on traditional virtual machines (VMs) or bare metal servers. They also enable developers to develop applications in one environment, deploy them in another, and run them anywhere.