The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.
Microsoft 365 services help companies worldwide improve business and revenue by providing best in class digital workspace experience. The NiCE Active 365 Management Pack complements this by advanced M365 monitoring such as full Teams Call analysis integrated into Microsoft SCOM. Advanced monitoring and analytics help you reveal unwanted micro-events influencing the health and performance of the system and its users.
Many organizations use Kubernetes to orchestrate their containerized applications. But because Kubernetes is complex, application developers may take some time to ramp up on the intricacies of monitoring a Kubernetes environment. This means that teams often need to create internal documentation and offer hands-on training to bridge the knowledge gap.
Service virtualization is not new. In fact, the concept and technology were established 20 years ago. At its core, service virtualization offers the ability to simulate behavior, data, and performance characteristics of applications and services. Through service virtualization, teams can ensure they have an on-demand environment to support their testing needs.
Redis is an open-sourced, BSD 3 licensed, highly efficient in-memory data store that can be easily used as a distributed, in-memory key-value store, cache, or message broker. It is known for being extremely fast, reliable, and supporting a wide variety of data structures, making it a very versatile tool widely adopted across the industry. Redis was architectured with speed in mind and is designed in a way that it keeps all the data in memory.
Microservices have grown to become one of the most optimal alternatives to monoliths. However, just building your app and releasing it to the public isn’t everything. Monitoring microservices is as important as building and releasing them. You need to maintain it to resolve issues that may occur and also introduce new features from time to time.
In this tutorial we’ll learn how to use Python to get time series data from the OpenWeatherMap API and convert it to a Pandas DataFrame. Next we’ll write that data to InfluxDB, a time-series data platform, with the InfluxDB Python Client. We’ll convert the JSON response from our API call to a Pandas DataFrame because I find that that’s the easiest way to write data to InfluxDB.