If there’s one thing SRE professionals and Development engineers lack, it’s time. After all, engineers need to oversee a variety of processes—like ensuring operational stability, conducting integration testing, and maintaining cybersecurity—to make sure their apps are working optimally. The list goes on and on. With heavy workloads and tight deadlines, there’s little time to waste on software issues stemming from internal collaboration issues.

Leaders know that making good, fast decisions is challenging under the best of circumstances. But, the trickiest decisions are those we call “big bets” – unfamiliar and high-stakes decisions. When you have a crisis of uncertainty, such as the COVID-19 pandemic, which arrived at overwhelming speed and enormous scale, organizations face a potentially paralyzing volume of these big-bet decisions.

How do you manage a remote team? We asked 100+ managers to share their best advice for managing plans, priorities, execution when your team is remote.

In this article, I'll explain how to use a Postman collection I have created to load test our instance of our test API. The process is pretty straightforward, as is shown below. You need to feed your exported Postman collection to our postman-to-k6 converter, and use the generated k6 script to load test your own API.

A year ago, Harry Bagdi wrote an amazingly helpful blog post on observability for microservices. And by comparing titles, it becomes obvious that my blog post draws inspiration from his work. To be honest, that statement on drawing inspiration from Harry extends well beyond this one blog post – but enough about that magnificent man and more on why I chose to revisit his blog. When he published it, our company was doing an amazing job at one thing: API gateways.

The differences between microservices and web services deal with different concepts in modern application design. A microservice is a small, independent, application that performs a highly focused service as well as possible. A web service is an internet-based interface that makes the “services” of one application available to applications running on different platforms.

You did it. You have machine learning capabilities up and running in your organization. Success! What started as a few nascent experiments (and maybe a few failures) are now carefully constructed models racing along in full production—with the ability to scale into the hundreds or thousands of productional models in sight. Assembling your expert team of data scientists and custodians seems like a distant memory. Now you’re looking ahead to the future—growth, innovation, revenue!

It’s hard to believe that we are now over 30 years into data warehousing. In that time, we have seen major changes in tools to help user report on and analyse data. In the last twenty years, we have seen the evolution from reporting, ad hoc analysis and advanced analytics. Today, BI/Analytics is a mature market with self-service BI and visual analysis standards in most organisations with self-service data preparation also widely deployed.