Resiliency As the Next Step in the DevOps Transformation

Written by Nick Heudecker

August 15, 2022

We’ve reached the point in the DevOps transformation where efficiency and automation are no longer the highest objectives. The next step is engineering past automation and towards fully autonomous, self-healing systems. If you aren’t conversing about building this type of resilience into your systems and applications, there’s never been a better time than now to start.

Get the Most Valuable Insights From Your AIOps Tools

Over the last decade, we saw our industry work toward improving communication and collaboration between Development and Operations teams to streamline the software delivery process, from code development and testing to deployment and monitoring. The shift required a substantial increase in cooperation between departments, and this new step of integrating resilience into your systems will be no different — but it also requires an enormous amount of data.

Your AIOps tools can only work as well as the data they are fed. Chances are that your logs and metrics produce way more noise than signal, and you’re holding onto more data than you need or than is necessary to deliver valuable insights from your AIOps tools. Any work you can do upfront to clean the data up will go a long way.

Shifting the Mindset of Your Engineers From Features to Runtime

In order to move from automation to this new place of autonomy and resiliency, your entire DevOps team will have to have an SRE mindset, and you’ll have to incentivize any feature-focused members of your team to shift their thinking more towards the runtime environment of your product.

This can be difficult because developers typically focus on shipping new things and just throwing them over the fence to the SREs and IT Ops teams. They aren’t interested in the runtime environment and just want to build cool stuff. SREs on the other hand, are incentivized by uptime or other types of performance metrics. In addition, your security team uses an entirely different set of tools, incentivized by avoiding risk and recovering quickly. The importance of figuring out a way to get people to empathize with the other groups adjacent to them can’t be overstated.

An Observability Lake Promotes Collaboration and Insights Into Data

Having a fully optimized observability lake helps to create the sort of environment that will foster the collaboration that you’re looking for amongst the different departments of your organization. It is also the best way to ensure that your AI ops tools are running as efficiently as possible and getting the most insights they can from the data you collect.

When data is delivered to an observability lake, it’s stored in open formats that any solution can read. It gives organizations choice and control and serves as an umbrella option that delivers “enough” until you’ve decided which data is essential and where it needs to reside to provide the most value to your organization.

An observability lake allows you to explore different data sources and correlation across those sources — all in one easily accessible place available to each team. So instead of your developers only getting access to applications performance management or your infrastructure and operations teams getting access to Splunk, you can help bridge those various data silos. This way, you can answer much more robust questions and do more intensive investigations into your data. The shared nature of the observability lake naturally encourages collaboration and iterations that everyone can benefit from.

Using Cribl Stream and Cribl Edge to Build Your Observability Lake

Integrating Cribl Stream and Cribl Edge into your already existing data management tools is the easiest way to build an observability lake and get you on your way to building as much resilience into your systems as possible.

With Stream, you can improve the signal-to-noise ratio of your data, clean it up at its source and get data flowing how you need it to by filtering out what you don’t need. Eliminate duplicate fields, null values, and elements that provide little analytical value. Then, increase the value of what you choose to keep by enriching it with third-party sources like GeoIP and known threats databases. With Edge, you can consolidate and automate the discovery, exploration, and collection of logs, metrics, and traces and push processing upward from Stream to the endpoint, making your environment significantly more efficient.

With your newly optimized data observability environment, your engineers can collaborate more efficiently and focus more on creating a continuously functioning, autonomous runtime environment. Your AIOps tools can have a less chaotic pool of data to pull valuable insights from, and a more resilient system can become a reality for your organization.

The companies that took part in the DevOps transformation enjoyed benefits like faster time to market, better quality software, and more efficient operations. By focusing on resilience next, you can move from efficiency to autonomy and significantly increase the chances of your organization thriving in the future.

The fastest way to get started with Cribl Stream and Cribl Edge is to try the Free Cloud Sandboxes.

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