Tag: Observability

In reaffirming our commitment of security and availability to our customers and potential customers, we are excited to announce the successful completion of the Service Organization Control (SOC) 2 Type 2 report. Independent auditor Schellman & Company, thoroughly evaluated how SignalFx demonstrates excellence...

Monitoring cloud applications requires ingesting and analyzing data from hundreds to thousands of web services, many of which employ scale-out, elastic architectures built on cloud infrastructure, containers, and applications. Traditional approaches to setting alerting conditions have been confined to using charts...

SignalFx has achieved the AWS DevOps Competency in Monitoring, Logging, and Performance. Monitoring and alerting are key to creating a common language for DevOps collaboration, and SignalFx is working with Amazon to speed and enable enterprise digital transformation in the cloud.Adoption of SignalFx by large AWS...

It’s no secret anymore: metrics are one of the most powerful tools an engineering team can have at its disposal when building modern, distributed applications. From observability to monitoring and from insights to alerting, historical trends and forecasting, metrics and timeseries data have the potential to deliver...

SignalFx is the only real-time cloud monitoring platform for infrastructure, microservices, and applications. Read our blog and keep up on the latest advances in monitoring and alerting best practices.

As monitoring evolves from static checks towards metrics time series, we gain a rich source of data from which we can extract value. This blog covers CPU utilization.

SignalFx has been running Docker containers in production since 2013. Every single application we manage executes within a Docker container. Along the way, we’ve learned how to monitor our Docker-based infrastructure and how to get maximum visibility into our applications, wherever and however they run...

Reducing Alert Noise: Rates of Change

Monitoring modern infrastructure poses fundamentally new challenges in terms of data volume and velocity. Collecting the metrics emitted by machines is only the first step. To extract value from that data, we need a method of expressing service, team, or business goals against that stream of data...