Operations | Monitoring | ITSM | DevOps | Cloud

15 November 2022: Canonical announced today the availability of new enterprise-grade Ubuntu images designed for next-gen Intel IoT platforms. Purpose-built for industrial environments and use cases, the latest Ubuntu images on Intel hardware deliver the performance, safety, and end-to-end security enterprises expect from the most widely used Operating System (OS) among professional developers with latest Intel technologies pre-enabled and available.

With AWS re:Invent right around the corner, infrastructure has been top of mind at Cortex. Earlier this year, we launched our revolutionary Resource Catalog, which integrates with AWS accounts to automatically ingest all infrastructure components, from s3 buckets to lambdas. Through this process, Cortex allows you to track everything in a single place, while augmenting the information that already exists in AWS. The Resource Catalog surfaces live information about your infrastructure assets.

Mean time to resolution (MTTR) is a metric that transcends industry and technology. It’s a measure of how quickly, on average, support teams identify, act, and resolve IT issues and incidents. Because MTTR directly relates to service quality, maintaining a low MTTR is a critical goal for DevOps and SRE teams. These teams have a vested interest in resolving issues quickly because escalating incidents to higher levels of the support team increases response and resolution times.

Relational database or non-relational database: which should you use for your projects? It’s a common question. When choosing the database type that’s right for your requirements, it’s important to understand the differences between the two. Both database types are practical in different situations and use cases and have commonalities.

Datadog Real User Monitoring (RUM) provides end-to-end visibility into the user experience and performance of your browser and mobile applications. RUM allows you to capture and retain complete user sessions for 30 days. This means you can pinpoint bugs, prioritize issues, and determine fixes with data collected across an entire quarter.

The use of cloud computing by financial institutions has significantly increased in the last few years, a trend that was further accelerated by the COVID-19 pandemic. In the next few years, financial institutions will need to continuously balance the pressure to innovate quickly while managing risk and combating financial crime.

In Part I of this two-part series, I talked about the key benefits of top-down cost allocation: It starts at the provider level, incorporates every penny of your cloud spend, and lets you break it down at as granular a level as is useful for your business.

Data is becoming increasingly essential to businesses globally, allowing for insights to be gathered around critical processes and operations. Over time, the traditional systems put in place to hold our data have become unsuitable for modern-day needs due to the continuous growth of data. Edge computing has emerged to reshape the current computing environment and allow data to be processed closer to where it’s being generated.

Cloud costs can pile up quickly, and often behind the scenes. We explain how software-defined cloud interconnect (SDCI) can help your business rein in a swelling budget.

If you’re writing software today, then you likely use a CI/CD pipeline to build and test your code before deploying it to production. Having a fast and efficient build pipeline saves you development time, shortens feedback loops, and helps you ship features faster. Conversely, slow and unreliable build pipelines are full of lost productivity and sadness.