According to the 2021 test automation report, more than 40% of companies want to expand and invest their resources in test automation. While this doesn’t mean manual testing is going away, there is an increased interest in automation from an ROI perspective – both in terms of money and time. After all, we can agree that writing and running those unit test cases are boring.
The Splunk Threat Research Team (STRT) has continued focusing development on the Splunk Attack Range project and is thrilled to announce its v2.0 release with a host of new features. Since the v1.0 release 6 months ago the team has been focused on developments to make the attack range a more fully-featured development testbed out of the box. This blog post will share these additions as well as some of the project’s future directions.
According to recent surveys and reports on the industry, Kubernetes and containers are more popular than ever. Containers and serverless functions are being mainstream and ubiquitous – with a more than 300% increase in container production usage in the past 5 years. This trend is especially true for large organizations, which are often using managed platforms and services.
Network performance monitoring (NPM) and application performance monitoring (APM) are both key pillars of an overall performance and reliability management strategy, especially when dealing with complex, distributed infrastructure across cloud-native environments. NPM and APM also complement each other, in the sense that NPM can serve as an additional source of truth and observability for application performance.
Anomaly detection can be defined by data points or events that deviate away from its normal behavior. If you think of this in the context of time-series continuous datasets, the normal or expected value is going to be the baseline, and the limits around it represent the tolerance associated with the variance. If a new value deviates above or below these limits, then that data point can be considered anomalous.
In many critical areas, you can automate the completion of repetitive chores in an efficient and effective manner by using a computer language such as Python. When you are just starting out, it’s vital to understand the fundamentals of Python via coding examples. However, if you want to improve your Python skills, you should concentrate on constructing things and automating real-world tasks.
At Honeycomb, we love involving our customers when we can. We asked: “What was the first thing you found value from with Honeycomb?” Here are some of our favorite answers.
OpenTelemetry is one of the most fascinating and ambitious open source projects of this era. It’s currently the second most active project in the CNCF (the Cloud Native Computing Foundation), with only Kubernetes being more active. I was at KubeCon Europe last month, delivering a talk on OpenTelemetry and it was amazing to see the full house and the excitement and interest around the project.
With the growing pace of tech-oriented companies, software development is picking up. Many new tech stacks are coming into the world to make the development process easier, and a lot of these new companies are using PHP as the backend framework for their apps. PHP, with its various version updates, has grown popular among developers. Most PHP developers have heard and worked with Laravel at least once.
