Lots of updates this month including a rewritten alerts system, auto discovery, private packs, wildcard metric support and a bunch more of small changes.
Grouping things up into easily deployable artifacts isn't a new concept. Most monitoring tools mature to the stage where you can import pre-baked configuration that helps speed up monitoring of services. We recently added a Packs Library to Dataloop that uses our unique plugin deployment technology to single click install monitoring for common services. On top of this we added auto-discovery to automate the installation of packs. So now you can get from no monitoring to a base coverage for your common services across thousands of hosts in a matter of minutes.
Everyone loves example dashboards! So we've decided to do a series to highlight a few of the coolest ones. If you're easily bored skip to the bottom set of links to get your dopamine fix of screenshots. For those with a longer attention span hopefully this post helps explain some context.
Monitoring at scale is a hard task so we often get asked by people what our architecture looks like. The reality is that it's constantly changing over time. This blog aims to capture our current design based upon what we've learnt to date. It may all be different given another year. To provide some background we initially started Dataloop.IO just under 18 months ago. Before then we had all been involved in creating SaaS products at various companies where monitoring and deployments were always a large part of our job.
To set some context I've been working with developers for the past 14 years inside software companies, usually on the operations side of the fence. I've watched first hand how every one of those companies has tried to transition from creating on-premise software to running a SaaS service.