Operations | Monitoring | ITSM | DevOps | Cloud

What a way of starting the year! Setting milestones, helping those in need, and daring to dream. January 2022 starts with one of the biggest technological conferences — CES. So, in this piece, you will find a breakdown of three robots in our usual style. But there’s more… we also bring a story to inspire you all. It’s a great experience writing this blog, where every month news are abundant. Thank you all for contacting us and sharing your stories.

Many organizations leverage AWS to build fully managed, event-driven applications, which break down complex workloads into APIs, event streams, and other decentralized services in order to improve performance and scalability. This type of architecture relies primarily on AWS Lambda functions to process synchronous and asynchronous requests as they move between a workload’s resources, such as Amazon API Gateway and Amazon Kinesis.

Engineers who support production environments are tasked with resolving new issues as quickly and efficiently as possible. But as they look to carry out these responsibilities, their remediation workflows tend to take on the following pattern: For example, someone on your team might discover in a log analysis tool that a user is flooding a key service by making an abnormal number of requests.

To start, DevOps is exactly what it sounds like — a combination of software development and IT operations that shortens the product lifecycle and delivers a higher quality operational performance, benefiting the company and customer alike.

In an earlier blog post, we had discussed how server performance monitoring is not just about monitoring CPU, memory, and disk resources anymore. There is more to server performance monitoring than just three resources or metrics. That blog post covered several key performance indicators (KPIs) that IT teams must track to ensure that their servers are performing well. In this blog post, we focus on another KPI – server uptime.

Before continuous integration came to be, setting up builds was no fun because the complexity and overhead involved in a release cycle was compounded by inflexible, manual processes. The release cycle was slow and often resulted in breaking changes. Continuous integration and continuous delivery (CI/CD) has changed much of that through pipelines that automate how we build and test software—today, we can deploy, have builds fail, and resolve any errors faster than ever.

Maintaining the reliability of complex services just got easier with Operational Readiness Checklists. Service owners and engineering leaders can now evaluate and maintain the production readiness of the services their users rely on every day: spot risks in your service dependencies before they cause incidents, and respond quickly if they do. Before you put a new service into production, readiness checklists help you dot-your-is and cross-your-ts.

Welcome to Part II of this three-part blog series on adopting the low latency Ubuntu kernel for your embedded systems. In case you missed it, check out Part I for a brief intro on preemptable processes in multiuser systems and memory split into kernel and user space. The low-latency Ubuntu kernel ships with a 1000 Hz tick timer granularity (CONFIG_HZ_1000) and the maximum preemption (CONFIG_PREEMPT) available in the mainline Linux kernel.

Over the last two years much of the Global workforce has experienced remote working first-hand. Sound familiar? For many, this was a ‘career first’, changing their views on the effectiveness of remote working. The desire to be office based has reduced dramatically with people wanting to avoid time-consuming commutes. In a recent survey, a staggering 91% of US workers wanted home working to persist post pandemic.

There’s no one-size-fits-all incident response process. Depending on your organisation’s shape and size, you’ll have different requirements and priorities. But the same three pillars form the core of any good process, whether it’s for the largest e-commerce giant or a scrappy SaaS startup.