Continuous improvement is one of the fundamental tenets of Agile methodology that PagerDuty’s product development teams emphasize. This already works fairly well at the individual team level via retrospective meetings and postmortems but sometimes we don’t notice larger or systemic issues that are outside the control of a single team. This blog will share the process that we use at PagerDuty to uncover those issues, the outcomes we have seen, and how we have evolved that process.

Software engineering is a skilled task; those who obtain the experience and credentials necessary to become engineers know this, as do their employers. Engineers have an overarching goal of using these skills to construct experiences that enable end-users to complete a task successfully and they hope to provide enjoyment and comfort along the way. Anyone who has written software used by a decent number of people knows how daunting this task is.

Akamai mPulse is a real user monitoring (RUM) service that enables organizations to get deep visibility into end user experience across their websites or applications. With mPulse, businesses can collect high-granularity metrics directly from their users’ browsers, and then analyze that data to pinpoint slow resources (e.g., third-party scripts), track user engagement, and make decisions to improve the performance of their products.

Do you want to improve your user satisfaction and boost your Google page rankings? You’ve come to the right place. A better user experience starts with your website’s time to first byte (TTFB). We’ve got 6 actionable tips to help you measure and improve your time to first byte (TTFB).

With the release of Grafana v6.3, we are introducing a significant improvement to Loki’s log exploration workflow in Grafana Explore. Launched at KubeCon North America last December, Loki is a Prometheus-inspired service that optimizes storage, search, and aggregation while making logs easy to explore natively in Grafana. Loki is designed to work easily both as microservices and as monoliths, and correlates logs and metrics to save users money.

The online world is full of contrasts. On the one hand, you have site reliability engineers whose job is to keep the business running by ensuring an app’s smooth operations. On the other hand, you have the DevOps staff, whose goal is to minimize cycle time—the time from business idea to feature in production. These two teams can have conflicting objectives.

Maybe your company just merged with another organization. Or perhaps you were a member of a group that was just bought out. Chances are you’ve been there: more than 75 percent of industries have become more concentrated since the late 1990s and the mergers and acquisitions boom is only expected to grow.

Since Lambda added SQS as an event source, there has been a misconception that SQS is now a “push-based” service. This seems true from the perspective of your function because you no longer have to poll SQS yourself. However, SQS itself hasn’t changed – it is still very much a “poll-based” service. The difference is that the Lambda service is managing the pollers (and paying for them!) on your behalf.

If you have anything to do with the world of cloud computing or even programming for that matter, then I’m sure you’ve heard of different terms being tossed around such as “serverless computing” or “containers,” and even “monolithic architectures.” A lot of people who understand such computing methods can have a bad habit of using these terms without leaving any explanation as to what they are.

As multi-cloud architectures grow in popularity, more and more organizations will start asking how to secure multi-cloud environments. Some will conclude that a multi-cloud architecture requires a fundamentally different approach to cloud security. That’s one example of a myth about cloud security in a multi-cloud architecture. Let’s take a look at why this assumption is flawed, along with some other common myths about multi-cloud security.