Systems monitoring, APM, web and user monitoring, error detection, log management – today’s monitoring stack is complex, fragmented, and noisy. With multiple tools creating a flood of disparate alerts, IT teams struggle to identify and resolve incidents in a timely manner. This can result in poor uptime and angry customers. BigPanda automatically consolidates and correlates IT alerts from all of your various monitoring systems into unified, high-level incidents so that you can instantly detect critical issues and resolve them more quickly.
Once adopted, BigPanda becomes a critical part of a company’s monitoring stack. As such, our application is expected to be up and running 24/7 and to be able to detect and solve any operational issues as quickly as possible. As a result, our own monitoring stack has to be top notch, and log aggregation is a central part of the monitoring of that stack.
BigPanda has more than 20 microservices running in production with a large AWS deployment that generates 30 GB of log data every day. It is crucial to be able to identify issues with specific microservices in real-time, so we deployed the ELK Stack to help to centralize the monitoring of all the pipelines in one dashboard and troubleshoot issues in production.
BigPanda initially started out with ELK on a small scale. But as we expanded, it quickly became apparent that running the stack could not remain a one-man operation. Instead of spending the resources on maintaining the Elasticsearch cluster, BigPanda decided to opt for a hosted ELK solution that provided the stack as an end-to-end service. Logz.io answered these requirements, thereby freeing our developers to concentrate on what really matters — improving our product.
“With Logz.io, we were able to triple our infrastructure and team without a moment’s thought on scaling our logging infrastructure. Logz.io saved us thousands of hours that would have otherwise been spent on maintaining and running ELK.”
Logz.io makes transitioning to its cloud ELK easy, so the move from our existing stack was smooth. All we had to do was forward the data from our Logstash instances to Logz.io’s listeners instead of to our own Elasticsearch cluster. It was that simple!
In addition to logging and troubleshooting, BigPanda uses Logz.io for the tasks of triageing and prioritizing support cases — tasks that previously consumed time and involved a time lag due to the global distribution of our company.
The sales and customer success teams at BigPanda use Logz.io to analyze user behavior and success. By monitoring error messages in Kibana, our sales engineers can assess which customer is experiencing issues and what kind of challenges they are facing with the product. The ability to get insight in real time into how our customers are interacting with the product is priceless. It greatly enhances our ability to provide optimized support.
“The ability to get insight in real time into how our customers are interacting with the product is priceless. It greatly enhances our ability to provide optimized support.”
In total, more than 50 users at BigPanda now access and use Logz.io on a daily basis. With Logz.io, we were able to triple our infrastructure and team without a moment’s thought on scaling our logging infrastructure. Logz.io saved us thousands of hours that would have otherwise been spent on maintaining and running ELK.