Systems | Development | Analytics | API | Testing

Kubernetes is a powerful platform that comes with many features to help engineers run their applications more efficiently. However, as you gain more experience and deploy more workloads, you’ll inevitably start looking for ways to optimize Kubernetes performance. There are many ways to approach optimization. On one hand, you could work exclusively with the tools and configurations provided by Kubernetes itself; on the other, you could reap the benefits of third-party tools.

While ‘Smoke Testing’, ‘Sanity Testing’, and ‘Regression Testing’ are widely practiced in the world of QA, there still exist some misinterpretations or misconceptions around these concepts. For your team to make the most optimal use of the 3 methods, this article will help you to understand and differentiate them from one another.

More than 70% of automation script failures are false and are caused by poor automation design practices. The False Failure Rate (FFR) is the percentage of tests that falsely fail when a test suite is run. It is calculated by dividing the number of false failures (failed tests that actually passed) by the total number of tests that were executed during a test run. Higher false failure rates increase testing and maintenance costs.

Ever since computers started to make their way into the workplace, application development has been a priority for businesses. As a company grows, there is a need for new applications to handle new business processes and make the most of new opportunities. However, the list of custom applications requested can turn into a backlog as you hit the limits of developer resources. To ease this burden, the concept of low-code application development has entered the enterprise.

As the field of software development continues to evolve, one cannot help but wonder about the future of the profession. With the integration of AI tools, like ChatGPT, and Machine Learning, tasks that were once exclusive to human developers are now being handled by machines. While this has the potential to greatly increase efficiency and productivity, it also raises important questions about the future of the developer profession. How will developers adapt to this new reality?

The most successful organizations today know they need to use business analytics to make decisions and drive outcomes. Often, however, these decisions must be driven by insights that can remain hidden in data. That’s where data mining comes into play. Data mining is a powerful tool to help extract meaningful insights from even the largest, most complex data sets.

It began with the pandemic. Consumer spending shifted from experiences to goods, spiking demand. Ports clogged due to shutdowns. Factories operated at reduced capacities from sickness, lockdowns, or even infrastructure issues like rolling blackouts. But it didn’t end there. The ripple effects have continued. Supply chains continue facing upheavals, particularly with respect to inflated prices and global conflict like the war in Ukraine. This leaves supply chain professionals on shaky ground.

“If you don’t care about quality, you can meet any other requirement” Gerald M. Weinberg Software testing has never been a walk in the park. It demands expertise in software development, users’ behavior, the software under test architecture, stakeholders’ requirements, and programming knowledge. Quality teams know firsthand how challenging it is to ensure software quality when its architecture is becoming more complex and teams are releasing at a pace faster than ever.

Organizations need to manage data across ecosystems, develop data pipelines, APIs, insight into their metadata, and try to make sure that silos and data quality issues are managed effectively. Enter data observability platforms. This blog post looks at what drives many organizations to adopt data observability to ensure the health of your data across systems and providers.