Operations | Monitoring | ITSM | DevOps | Cloud

This article was originally published in The New Stack and is reposted here with permission. Hundreds of billions of sensors produce vast amounts of time series data every day. The sheer volume of data that companies collect makes it challenging to analyze and glean insights. Machine learning drastically accelerates time series data analysis so that companies can understand and act on their time series data to drive significant innovation and improvements.

Communication within organizations has evolved from email threads to real-time chats. With the vast potential of real-time messaging, organizations are beginning to explore how they could make these chat applications do more. We now even have a name for this phenomenon — ChatOps. To get a taste of the power of ChatOps in action, let’s build a ChatOps bot that sits in a Mattermost channel with Errbot.

As more workloads move to the cloud, more employees work remotely and SaaS application adoption increases, enterprises have had to rethink the way they secure access to the IT services their employees need. The reality for most enterprises today is a multi-vendor, siloed approach that has only compounded their security and IT operations management headaches as they end up using multiple management tools to secure and govern their access to cloud services.

Written by Andrew Puch and Brian Langbecker You use NGINX as a proxy for your application, and you want to leverage your favorite features in Honeycomb to help make sense of the traffic data. Have no fear: Honeycomb is more than capable and ready to help! Things you will need: Before you start with the instructions, let’s discuss a lightweight tool called Honeytail. This utility will tail log files, parse the various formats, and send the data to Honeycomb.

As a DevOps engineer or SRE, you are often faced with investigating complex problems — mysterious application performance issues that happen intermittently or to only certain portions of your application traffic — that impact your end users and potentially your company’s financial targets. Sifting through hundreds or even thousands of transactions and spans can be a lot of tedious, manual, and time consuming investigative work.

As web applications become more complex, software engineering teams must rely on many different products and services to create the best developer experience. The application development ecosystem has grown beyond version control and hosting deployment. Manually managing the deployment of new features across all services can create a serious bottleneck in the software development lifecycle. It also introduces the risk of human error.