Operations | Monitoring | ITSM | DevOps | Cloud

Recently I've been working with an MSP on a couple of their use cases. One of the services they're designing for their customers is that of cost optimization on their IT infrastructure. On top of the in-house services and tools they had, they needed a tool to help them identify and quantify this underutilization. This is something SquaredUp is very well suited to do – and so we got to work!

Many organizations use Oracle databases for their ability to be deployed anywhere, embedded security features, robust data analysis capabilities, and scalability. But manually managing Oracle databases can be impractical, requiring constant attention to optimize performance.

Guide for kubectl logs with a cheat sheet. Learn to efficiently debug and monitor Kubernetes pods, from basic commands to advanced techniques.

According to the 2024 State of Production Readiness report, 54% of engineering leaders said poor production readiness standards contributed to a decrease in developer productivity. But how? If software falls out of alignment with best practice—including those designed to maintain the health, observability, and security of software—developers wind up spending more time finding information and fixing issues than building new value.

Late last year, Datadog announced something called Flex Logs, a “more affordable” warm storage tier for log data. Designed for high-volume datasets that are infrequently queried and don't require real-time analysis, the Flex Tier offers Datadog Log Management customers a third option for data storage.

When managing IT infrastructure, one crucial aspect is ensuring that your incident alert management system remains operational during critical failures or outages. Relying on a single cloud provider for both your primary services and incident management can create a significant vulnerability. If that cloud provider experiences an outage, your alert management system could become inaccessible precisely when it’s needed most, leading to delayed responses and extended downtime.

Monitoring used to be so easy. Servers had names and lived down the hall, or across the street. If things weren’t working, you could turn them on and off again. Database filling up? Just throw another hard drive in there. Too many simultaneous requests? Rack another server and install a cache. Fast forward a couple decades, and things have gotten much more complicated.

That’s the assessment of Senior DevOps Engineer and Logz.io user Armin Morattab when discussing the impact of AI on his day-to-day job. He dives deep on AI, observability, and strategies for improving workflows with Logz.io Co-founder Asaf Yigal in our webinar, AI in Observability: Real Engineers Talk Real Uses Cases.

All eyes are on generative AI. Enterprise IT teams are looking to Gen AI to translate the high volume of data from their services architecture into actionable insights. The goal: Improve operational efficiency and quality of work. But it’s challenging to sort through the hype (and confusion) to identify which vendors have GenAI capabilities that can provide true impact and value to their IT and service operations. One capability in particular is AI-powered copilots.

DevOps automation is essential for speeding up delivery, minimizing errors, and boosting team collaboration. But selecting the right approach can make or break your organization’s agility and scalability. Let's break down three key approaches—DIY with Infrastructure-as-Code (IaC), Platform-as-a-Service (PaaS), and DevOps Automation Platforms—so you can identify the best strategy for your needs.