Operations | Monitoring | ITSM | DevOps | Cloud

You’ve pored over the MongoDB documentation, crafted highly polished and well-tuned queries, and confidently deployed your new code to production. Everything ran great at first, but once CPU or RAM usage hit a certain point, your queries suddenly slowed to a crawl. What happened, and how can you prepare for situations like this in the future? This is an unfortunate but common scenario with databases like MongoDB.

Machine Learning (and deep learning) applications are quickly gaining in popularity, but keeping the process agile by continuously improving it is getting more and more complex. There are many reasons for this, but primarily, behaviors are complex and difficult to anticipate, making them resistant to proper testing, harder to explain, and thus not easy to improve.

It’s no secret that AWS Lambda adoption has grown steadily since AWS first released it in 2015—and for good reason. The benefits of adopting Lambda are many: leveraging Lambda eliminates the need to provision and manage servers, enabling teams to just focus on their code without the mental and operational overhead of worrying about the underlying infrastructure.

Large-scale cloud applications are usually built using interconnected services that can be rather hard to troubleshoot. When a service is scaled, simple logging doesn’t cut it anymore and a more in-depth view into system’s flow is required. That’s where distributed tracing comes into play; it allows developers and SREs to get a detailed view of a request as it travels through the system of services.

IT service management (ITSM) has evolved into a much broader discipline than just delivering IT services to the business. It’s grown beyond the IT department and become an integral part of how every employee in almost every business unit performs and completes tasks. In a recent webcast, SolarWinds Sr. Solutions Engineer Liz Beavers, ITSM experts Valence Howden and Julie Mohr, and HDI’s Group Principal Analyst Roy Atkinson paint a picture of what can be expected for the future of ITSM.

Since the inception of JFrog – with OSS Artifactory – we’ve been adamant that you simply cannot deliver software with any type of scale, speed, or reliability without a robust artifact management solution. Now, over a decade later, other vendors in the industry are finally starting to catch on. AWS recently announced its CodeArtifact service for binary management.

Here at Moogsoft, we take quality seriously and one of the most important goals for our test suites is to catch issues early on in the development process. A lot of our automated tests are integrated into our CI/CD (Continuous Integration/Continuous Deployment) pipeline as gates that can block a merge request with quality issues. Therefore, to ensure stable CI/CD pipelines as well as quick and quality releases to production, it is important to have tests that are stable and lightweight.