Operations | Monitoring | ITSM | DevOps | Cloud

Mapping service vulnerabilities with Mend

Mend is an automated vulnerability scanning tool that helps teams detect and resolve issues quickly. Mend can discover outdated packages and tell you if you’re relying on tools with known issues. Then, through automated remediation, Mend creates pull requests for developers with specific guidance on resolving those issues. Mend conducts static code analysis as well as package and dependency management analysis to identify weaknesses.

The underappreciated power of technical project managers

Imagine you’re part of a software development team that’s working on an important new project. Everyone is excited about the work, but you’re running into trouble. The work wasn’t clearly divided up, so some of the engineers unintentionally did overlapping work. Meanwhile, neither the PM nor the engineers realized that they would eventually need sign-off from an external stakeholder, who doesn’t agree with all of the project requirements.

How reporting enables informed decision-making

For software development teams to make meaningful progress, they must invest in efficient monitoring, reporting practices, and tooling. This is because only by keeping track of select metrics, such as those pertaining to application performance, will you know whether you are on the right track. Without knowledge of whether the software is functioning and performing as it is supposed to, there is no way of knowing what, if any, changes need to be made.

Picking the right developer workflow tools for your team

The software development life cycle is by no means a short or easy process. From project design to post-production monitoring, each stage of the life cycle comes with its own set of workflow-related demands. Teams must develop adaptable yet agile workflows to prioritize efficiency while simultaneously ensuring a positive developer experience. Thankfully, there is no shortage of tools to boost productivity in software development team workflows.
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Introduction to Automation Testing Strategies For Microservices

Microservices are distributed applications deployed in different environments and could be developed in different programming languages having different databases with too many internal and external communications. A microservice architecture is dependent on multiple interdependent applications for its end-to-end functionalities. This complex microservices architecture requires a systematic testing strategy to ensure end-to-end (E2E) testing for any given use case. In this blog, we will discuss some of the most adopted automation testing strategies for microservices and to do that we will use the testing triangle approach.

Documenting your APIs with developer API portals

Developers need all the information they can get on APIs to get them to work in alignment with their vision. There is no dearth of knowledge in the software development industry today, but only when that knowledge is documented well is it of use to others. In the context of APIs, developer portals offer an effective way to document and communicate relevant information. In this article, we answer the following questions about developer API portals.

Cracking Performance Issues in Microservices with Distributed Tracing

Microservices architecture is the new norm for building products these days. An application made up of hundreds of independent services enables teams to work independently and accelerate development. However, such highly distributed applications are also harder to monitor. When hundreds of services are traversed to satisfy a single request, it becomes difficult to investigate system issues.

How to monitor Microservices?

Microservices are being used every where and for good reasons. They do provide you with many benefits especially improved focus and cutting the time to market. Microservices do bring complexities too. Monitoring microservices is complex because of simply the number of them. Monitoring a user transaction requires monitoring many microservices. Correlating the data from them to identify the root cause manually is a nightmare especially in a complex environment with 100s or 1000s of microservices.