Systems | Development | Analytics | API | Testing

Google's Cloud Functions let developers run their code in production in a scalable way without worrying about the minutiae of server administration. In this article, Subomi shows walks us through building a real-world service using GCF.

Software testing was initially a manual activity, however, due to the importance of speedy delivery, the transition to automation testing is predictable. Unfortunately, this shift can become an overwhelming voyage, especially for startups without a dedicated QA team. Suppose you are a manual tester (or developer) seeking an automation solution to speed up your project.

Tired of spending hours on creating GA reports? Here’s how Google Analytics users can make the most out of automated reports.

In the last blog post, we discussed the need for both speed and quality for your API delivery and how APIOps can help achieve both. In this part of our blog post series, we’ll walk through what the API lifecycle looks like when following APIOps. We’re still following the best practice we’ve established in the industry over the years, but what you’re going to see is that the processes we follow at each step of the API lifecycle – and between each step – have changed.

At Speedscale, we are on the cutting edge of defining autonomous testing for the cloud era. However, we aren’t the only company trying to solve this problem and we enjoy learning from every perspective. That’s why Facebook’s recent blog article about autonomous testing caught my eye. They’ve built a sophisticated autonomous test system that introduces many of the same techniques we utilize.

When you need to get new features and products out the door fast, API management is the driving force to keeping everything on track. According to the "State of API Integration 2020" report by Cloud Elements, 83 percent of respondents say that API integration has become a critical part of their business strategy as they move forward on their digital transformation efforts. Leveraging integrations is what helps these companies agile and able to respond quickly to customer demands.

By Jean-Baptiste Thomas, Pure Storage & Yaron Haviv, Co-Founder & CTO of Iguazio You trained and built models using interactive tools over data samples, and are now working on building an application around them to bring tangible value to the business. However, a year later, you find that you have spent an endless amount time and resources, but your application is still not fully operational, or isn’t performing as well as it did in the lab. Don’t worry, you are not alone.

There are many good uses of data. With data, we can monitor our business, the overall business, or specific business units. We can segment based on the customer verticals or whether they run in the public or private cloud. We can understand customers better, see usage patterns and main consumption drivers. We can find customer pain points, see where they get stuck, and understand how different bugs affect them.

With the launch of the Cloudera Public Cloud 7.2.12, the Streams Messaging for Data Hub deployments have gotten some interesting new features! From this release, Streams Messaging templates will support scaling with automatic rebalancing allowing you to grow or shrink your Apache Kafka cluster based on demand.