December 26, 2022
The rise of modern applications has kicked basic monitoring tools to the curb. With observability, teams can proactively know, in real-time, what’s happening across the entire stack. Observability allows us to take a holistic view of our IT systems and learn about the current state based on the environment and the data it generates. But how do you properly implement observability? Here are 6 guiding principles to make sure your IT and DevOps teams are set up for success.
The best-telling factor of a sound observability solution is enhanced visibility. Visibility allows you to quickly identify where something is wrong. Observability tools are in place as an extension — to help you understand why something is wrong. An observability solution with end-to-end visibility helps you learn about your distributed systems. This in turn allows for better data analysis and a clearer understanding of the security, performance, and general health of your environment.
Observability tools in modern applications should detect performance issues, measure applications, and notify the DevOps team of any security risks before they occur. The transparency should also extend to sharing custom metrics and data sources between teams to enable a well-informed workflow. To put this in perspective, let’s imagine an error alert in your time series data of stock prices. The ideal observability system will allow teams to trace the entire data to find the root problem.
A good observability strategy also allows teams to move between high-level and lower-level views of application performance through a transparent path. When something breaks, DevOps teams don’t have to waste precious time trying to find where the problem is occurring, and can focus on fixing the problem at hand. Visibility makes this possible.
Data curation and reshaping help translate and transform data to be usable. This means organizations should have their information curated and designed in the easiest usable format. A good observability platform should cut down the raw data to the relevant size and format. This lets teams both find the problem and easily generate insights. Teams should also be able to add custom telemetry data sources to their observability system.
Logs, metrics, and traces are the three pillars of a modern observability pipeline, they make data easy to retrieve at any point. They also contain data in different formats. For instance, logged data is unstructured and in one central location while metric data has been structured to quantify application performance. Trace data allows the administration to measure the response time of the application across the system. Having a solution that can collect your data from all different sources and shape it into actionable logs and metrics for analysis, makes it possible to use that data effectively across all your observability and security tools.
A single source of truth (SSOT) is a system where everyone within an organization bases business decisions on the same telemetry data. It works as a central informational repository that allows a single truth source in all of the organization’s workings. Since organizations gather observability data in massive quantities, it is essential to create a pipeline to access the data streams. Teams can enable this by providing their engineering teams with a single source that stores the data points they need. They can further use an observability pipeline to create a reference point for this data. This can then be leveraged by all teams in the organization to get analysis, work collaboratively, and identify performance issues. Teams can also fetch only the data they need without being bombarded by all data.
SSOT can only work with an end-to-end platform that has a complete view and understanding of the environment in which it operates. It is important for modern observability platforms to be fully stacked, cloud-first or cloud-native, and end-to-end to ensure the best observability solutions. This is because performance issues occur in both the front end and back end of the system. Here, the end-to-end platform covers both and ensures no issue is missed.
Speed is everything when it comes to troubleshooting. The longer it takes to pinpoint and resolve an issue, the more detrimental it is to the business. Security teams work with endless amounts of data, from multiple sources, and in all various formats. It is an ordeal having to dig through piles of data to detect security breaches or threats and then have to mitigate them. Observability to the rescue.
When an application error occurs, observability can help jumpstart a quick response so the issue is mitigated immediately. Observability solutions that have data filtering capabilities can help get ahead of these issues. It acts as a universal collector that ingests and normalizes data. Then, enriches the data for it to land in the right security tooling, limiting noisy information. These two then allow for speedier threat intelligence and accelerated incident reporting.
Modern applications are constantly evolving and teams need to be agile and adapt. Observability is gaining more momentum and breadth, with new solutions arising for every new problem. Traditional tools can’t handle the vast amounts of data being generated, making it difficult to send data to third-party analysis vendors or move data to a new tool. To get ahead of bottlenecks, organizations need to implement an observability solution that is built around protocols first, and then around specific vendors as needed. This prevents vendor lock-in and allows DevOps teams to keep up with the pace of change and adapt easily.
Cribl Stream is a vendor-agnostic observability pipeline that gives customers the flexibility to route and process data at scale from any source to any destination within their data infrastructure. With extensive experience building and deploying log analytics and observability solutions for some of the world’s largest organizations, Cribl helps customers take control of their data to support their business goals. Contact Cribl today!