As Stephen Marsland once said, “if data had mass, the earth would be a black hole.” A vast part of the immense amount of structured and unstructured data that we call “Big Data” is nothing but machine-originated log data. Logs are generated for a lot of different purposes – from security to debugging and troubleshooting. They constitute a gold mine of useful information and actionable insights if properly stored, managed, and analyzed.

Last week I was at Monitorama and it was an amazing time! Not only where the talks fantastic, but I also had the opportunity to join Liz Fong-Jones from Honeycomb.io for a podcast with The New Stack to dig a little deeper into some of the topics in monitoring and site reliability engineering.

What are the essential things to monitor in your infrastructure? Sure, CPU utilization, memory usage, and IO throughput. However, once you notice a significant load somewhere in your infrastructure you want to know what is causing it, and that typically boils down to needing to find the process that’s using too much CPU or memory or that’s doing disk or network IO like there’s no tomorrow.

Amazon S3 is an object storage service widely used for storing many different types of data in the cloud. While it’s inexpensive and easy to set up and maintain S3 buckets, it’s also very easy to overlook S3 security aspects and expose them to malicious attacks. A typical example is accidentally allowing public access to S3 files. Several recent high-profile data breaches were caused by lax S3 security.

Pulling in data exposed via API is not one of the most common use cases for ELK Stack users but it is definitely one I’ve come across in the past. Developers wrapping their database services with REST API, for example, might be interested in analyzing this data for business intelligence purposes. Whatever the reason, the ELK Stack offers some easy ways to integrate with this API. One of these methods is the Logstash HTTP poller input plugin.