When your server goes down, it can certainly throw a wrench into your daily processes, costing you money and even causing you to lose customers until it’s back up and running again. Thankfully, Pandora FMS can help you prevent it from happening, and in the worst-case scenario when it does, you have the tools to get back up and running again in no time with our server monitoring solution!
As the business world continues to integrate AI and machine learning to better manage big data processes, one area that arguably has benefitted the most is business monitoring. From IT management to business intelligence, the last few years have seen a drastic shift in how companies are monitoring their data.
As we enter into our 4th year, we've decided to get up close and personal with our team to share with you their passion, drivers, lessons learned and significant moments of the past year. We're a young company dedicated to adding value in all corners that we reach, so we hope you find the upcoming series useful! Hey Marek, so can you tell us how long you’ve been at Dashbird and where you were before? M: I’ve been at Dashbird for two years now.
Yes, everyone is talking about the cloud, but are they actually ‘doing it’? The short answer is yes, and in stunning numbers. According to a recent O’Reilly survey, 90+% of organizations expect to increase their usage of cloud-based infrastructure. Over the next 12 months, 67% expect to shift half or more of their applications to the cloud, and 45% are planning to move three-quarters or more of their apps.
When it comes to data, let’s start with the obvious. Averages suck. As developers, we all know that percentiles are much more useful. Metrics like P90, P95, P99 give us a much better indication of how our software is performing. The challenge, historically, is how to track the underlying data and calculate the percentiles. Today I will show you how amazingly easy it is to aggregate and create SQL based percentile rollups with Postgresql and t-digest histograms!
In the field of open-source metrics and time series monitoring, it is quite clear today that Grafana is the most popular tool of choice. One of Grafana’s main advantages is its storage backend flexibility. It can support almost all the major time series datastores (Prometheus, InfluxDB, Elasticsearch, Graphite etc.), when each datastore has its own query language syntax, and slight differences in the actual Grafana UI and capabilities resulting from these differences.
This is a beginner’s tutorial for how to write static data in batches to InfluxDB 2.0 using these three methods. Before beginning, make sure you’ve either installed InfluxDB OSS or have registered for a free InfluxDB Cloud account. Registering for an InfluxDB Cloud account is the fastest way to get started using InfluxDB.
This is Part Two of Getting Started Tutorials for InfluxDB v2. If you’re new to InfluxDB v2, I recommend first learning about different methods for writing static data in batches to InfluxDB v2 in Part One of this Getting Started series. This is a beginner’s tutorial for how and when to write real-time data to InfluxDB v2. The repo for this tutorial is here. For this tutorial, I used Alpha Vantage’s free “Digital & Crypto Currencies Realtime” API to get the data.
Although network monitoring is an unenviable task, it must be done to sustain your network’s health. But what if you had a real-time report containing only key metrics, revealing how your network is doing without overloading you with information? This would (1) assure you that there are no aberrations in your network and (2) give you peace of mind.
