The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.
Our product strategy this year was relatively simple. Many observability practitioners we spoke with complained that observability was oftentimes slow, heavy, complex, and costly – which can be summed up in our CEO’s recent blog on modern observability challenges. While our customers didn’t report similar challenges, we wanted to further distance ourselves from this typical observability experience.
When we created Cribl Search, we wanted to give systems administrators the ability to query data without having to spend resources on collection and processing first — but we didn’t stop there. With Search, we’re also making it possible to query all the data you’ve already collected, processed, and kept in places like object stores, file systems, analytics tools, S3 buckets, or other data stores.
Tracing has always been a key use case for time series data. But admittedly, it’s also one that past versions of InfluxDB could not handle as well as we wanted. One of the roadblocks was the cardinality issue. Tracing data is, almost by definition, high cardinality data and prior to InfluxDB IOx, high cardinality data could affect query performance.
Amazon re:Invent is a major technology event every year. At this year’s re:Invent, the keynote by AWS CEO Adam Selipsky made a concerted effort to draw connections between technology and some of the key challenges that people around the world, and in some cases beyond the terra firma of Earth, face. While the presentation touched on a wide range of topics, one overarching theme was the intersection of the physical and digital worlds, and the role technology plays in bridging that divide.
When applications suffer performance degradation often the root cause of the issue is a database problem. In this guide we’ll show you 7 ways to troubleshoot your Azure SQL database performance issues using metrics and insights from the eG Enterprise monitoring solution.
Think about the last time your IT systems had an outage: How did your team react to it? Were they organized with a clear idea of how best to resolve the issue? Or was it chaotic, with people firing questions from all directions and customer service channels ablaze with requests for help? Digital technology disruptions are typical (and even expected) at the workplace, but it doesn’t have to be chaotic, with teams rushing around to extinguish the metaphoric fire.
