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When you combine the two and distill the technology at the core, what you end up with is the realization of the importance of logs and log management. This is because logs at multiple levels help DevOps teams understand their application and even allow them to detect and address application issues before being promoted into production. Containers, hooks within code, network activity, traditional infrastructure, development tools, etc. are all generating events and this data can provide any number of valuable insights for the enterprise. But why are log management solutions critical – not just a benefit – for DevOps?
As more enterprises adopt a DevOps culture, they become steeped in the mindset of collaboration, continuous delivery and integration, and broken-down silos, all to disrupt the older and slower methods of development. However, as we noted earlier in this blog, logs at multiple levels help DevOps teams catch issues before they arise, and use the log data to create a more user-friendly product or service.
But these are table stakes. Both log management and DevOps have been in use for long enough that it’s time to look beyond their core capabilities and expand to how they can be used together in more unique, differentiated ways. For example, leveraging machine learning capabilities like time series anomaly detection (TSAD): DevOps teams can use historical log data, such as web application traffic, to explore user engagements and detect anomalies among the noise. In this case, while single-metric TSAD is helpful information, multi-metric TSAD can provide another level of visibility for the DevOps team. In reality, data flows throughout an interconnected stack and using a tool that understands this is valuable. Devo can visualize the interconnection, and, based on multi-metric TSAD, alert the DevOps team as to what piece of the stack is causing the issue and what else it is affecting.
When all is said and done, DevOps’ use of log management will be used not just for CI/CD, but also for monitoring applications in real time and mapping other data – such as customer satisfaction scores (CSAT) – so that DevOps can truly get closer to the end user/customer.
Let’s use Devo as an example; we practice what we preach by viewing historical application logs over time and using TSAD coupled with real-time alerting to take action before real issues can arise. This gives us an understanding of how our platform is being used and sets a baseline of activity to help alert against potential issues.
But how to apply this to your company? Focus on two time frames for increasing value from machine-generated log data: the day-to-day analytics and the long-term approach.
For holistic, enterprise log management to take root in both CI/CD as well as real-time monitoring and alerting for DevOps teams, the log management platform needs to be able to easily ingest, query, alert on, and visualize data; it needs to be collaborative to uphold the DevOps culture; and it needs to be user-friendly. DevOps has proven to be a valuable team structure for companies, and the existing practices are valuable as well. However, as DevOps teams are consistently involved in improving the user experience, it means they need broad access to all data to do their jobs.
Find out more about how Devo works well with DevOps.
By Chris O'Brien
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